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Following references are cited in artwork [1,6,16,17,18,21,30,34,35,37,[42] [43] [44][45]48]. In recent years, imbalanced dataset classification has received significant attention due to its application in real-world problems, resulting in emergence of a new class of algorithms.

Classification algorithms that work with discretized data have been shown to yield better performance. Thus, discretization is often a critical technique in data preprocessing.

The proposed algorithm takes advantage of evolutionary multi-objective optimization to simultaneously optimize three objective functions: The first objective function uses AUC, instead of classification accuracy, to choose better cut points so as to identify the minority class.

The second objective function reduces the number of cut points while in the third objective function, low-frequency cut points are selected so that information loss caused by continuous to discrete data discretization is minimized.

To evaluate the proposed algorithm, 25 imbalanced benchmark datasets are totally used and the results are compared to those of popular algorithms in the literature such as Class-Attribute Interdependence Maximization CAIM and Evolutionary Multi-objective Discretization EMD.

Our findings indicate that the proposed algorithm outperforms the other techniques in terms of the number of cut points, AUC, and non-parametric statistical tests.

Rough-set classifier based on discretization for breast cancer diagnosis. Breast cancer is a kind of common malignant tumor of women.

It is becoming a leading cause of death among women. But the early detection and diagnosis of this disease can ensure a long survival of patients.

Classification plays an increasingly important role in machine learning and data mining. A rough-set classifier based on discretization RSCBD is proposed in this paper for breast cancer diagnosis.

It is built on fully considering the significance of condition attributes, classification attributes and attribute thresholds. Experiment results prove the RSCBD can get higher classification accuracy, lower reject rate, breakpoints and rules, which are important for breast cancer diagnosis.

Rough set-based approaches for discretization: The extraction of knowledge from a huge volume of data using rough set methods requires the transformation of continuous value attributes to discrete intervals.

This paper presents a systematic study of the rough set-based discretization RSBD techniques found in the literature and categorizes them into a taxonomy.

In the literature, no review is solely based on RSBD. Only a few rough set discretizers have been studied, while many new developments have been overlooked and need to be highlighted.

Therefore, this study presents a formal taxonomy that provides a useful roadmap for new researchers in the area of RSBD. The review also elaborates the process of RSBD with the help of a case study.

The study of the existing literature focuses on the techniques adapted in each article, the comparison of these with other similar approaches, the number of discrete intervals they produce as output, their effects on classification and the application of these techniques in a domain.

The techniques adopted in each article have been considered as the foundation for the taxonomy. Moreover, a detailed analysis of the existing discretization techniques has been conducted while keeping the concept of RSBD applications in mind.

The findings are summarized and presented in this paper. We investigate in this paper approximate operations on sets, approximate equality of sets, and approximate inclusion of sets.

The presented approach may be considered as an alternative to fuzzy sets theory and tolerance theory. Some applications are outlined.

A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks.

Its main goal is to transform a set of continuous attributes into discrete ones. The literature provides numerous proposals of discretization and some attempts to categorize them into a taxonomy can be found.

However, in previous papers, there is a lack of consensus in the definition of the properties and no formal categorization has been established yet, which may be confusing for practitioners.

Furthermore, only a small set of discretizers have been widely considered, while many other methods have gone unnoticed.

This paper provides a survey of discretization methods proposed in the literature from a theoretical and empirical perspective. We develop a taxonomy based on the main properties pointed out in previous research, unifying the notation and including all the known methods up to date.

Empirically, we conduct an experimental study in supervised classification involving the most representative and newest discretizers, different types of classifiers and a large number of data sets.

The results of their performances have been verified by means of nonparametric statistical tests. Additionally, a set of discretizers are highlighted as the best performing ones.

Discretization, defined as a set of cuts over domains of attributes, represents an important pre-processing task for numeric data analysis. Some Machine Learning algorithms require a discrete feature space but in real-world applications continuous attributes must be handled.

To deal with this problem many supervised discretization methods have been proposed but little has been done to synthesize unsupervised discretization methods to be used in domains where no class information is available.

Furthermore, existing methods such as equal-width or equal-frequency binning, are not well-principled, raising therefore the need for more sophisticated methods for the unsupervised discretization of continuous features.

This paper presents a novel unsupervised discretization method that uses non-parametric density estimators to automatically adapt sub-interval dimensions to the data.

The proposed algorithm searches for the next two sub-intervals to produce, evaluating the best cut-point on the basis of the density induced in the sub-intervals by the current cut and the density given by a kernel density estimator for each sub-interval.

It uses cross-validated log-likelihood to select the maximal number of intervals. The new proposed method is compared to equal-width and equal-frequency discretization methods through experiments on well known benchmarking data.

A Non-parametric Semi-supervised Discretization Method. Semi-supervised classification methods aim to exploit labeled and unlabeled examples to train a predictive model.

Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method, which adopts very low informative prior on data.

This method discretizes the numerical domain of a continuous input variable, while keeping the information relative to the prediction of classes.

Then, an in-depth comparison of this semi-supervised method with the original supervisedMODLapproach is presented. We demonstrate that the semi-supervised approach is asymptotically equivalent to the supervised approach, improved with a post-optimization of the intervals bounds location.

Discrete values have important roles in data mining and knowledge discovery. More specifically, building on prior research suggesting that.

In particular, we expected that people exposed to. Overall, the current research tested the hypothesis that anthro-.

More specifically, we tested a whether anthropomorphism and. We conducted a preliminary study to explore whether anthro-. Participants were a group of 15 regular slot.

Both regular and nonregular. Participants in both groups were. The participants were then asked to express their.

Appendix for the complete item descriptions. All regular players indicated that they usually played at least. All nonregular players indicated that they usually.

The regular players reported an. Next, we computed a t test comparing the two groups. Study 1 was designed as the first test of our hypothesis that.

Indeed, our preliminary study leaves the question of whether slot. Thus, in Study 1, we manipu-. We predicted that people would gamble.

Eighty-five participants 36 female;. The experiment used a one-way, two-level, between-. The independent variable was being primed about.

The dependent variable was the number of spins that the partici-. In Study 1, the effect of slot ma-. Participants were tested individually and.

More specifically, participants were informed that the researchers. For this purpose, participants were told that they could.

They were then presented with. Indeed, the slot machine can decide. Sometimes, she may choose to make fun of you, leaving you empty-.

In any case, the slot machine will always choose what will. You just have to start playing and see what happens. The other half of participants i. Indeed, the slot machine is con-.

Based on this algorithm,. In any case, the outcome of each. These texts were based on items related to intention and cogni-.

The remaining part of the instructions was identical in. After reading it, participants were reminded. To complete the task, they were told they could.

Participants in both conditions were pre-. When participants decided to quit the game by pressing the. They were asked to complete 15 items.

At the end of. First, in order to check the effectiveness of our manipulation, we. Then, we computed a second t test comparing the two experi-.

These findings suggest that presenting a gam-. Indeed, a few lines of text priming participants. Study 1 suggested that priming people to perceive a gambling.

To access the online slot machine, see http: Indeed, Study 1 did not test whether the presence of money would. For instance, it is possible that the strong motivational.

To address this important limitation, in. Study 2, we gave participants the chance to bet actual money to. Participants received an initial.

We expected that people would. Similar to Study 1, the experiment used a one-way,. The independent variable was. The dependent variable was the number of.

Presented on a computer screen,. The cover story related. Next, they were instructed that they would. Participants were informed that they had to spin the reels.

The amount of money. The equivalence between the slot machine. After being informed of the correspondence between the scores.

Participants were then automatically connected with a real. However, this time, when participants decided to quit the game,. Finally, participants were asked to.

First, we checked the effectiveness of our manipulation by. The analysis showed that participants in the anthropomor-. Thus, replicating the findings of Study 1, we found.

We further investigated in an exploratory way whether our. To do so, we computed. The analysis showed no. Unsurprisingly, we noted that on average partici-.

In sum, extending our previous findings, Study 2 showed that. The similarity of the findings in Study 1 and Study 2.

At the end of the experiment, participants were asked whether they had. Fourteen participants reported that they played less than once.

None reported a higher frequency of slot. However, we found no evidence for a link between slot machine. Study 3 aimed to further replicate the effect of slot machine.

Thus, in Study 3, we again. Furthermore, in Study 3 we tested. More specifically, we expected that people. The dependent variables were the number of spins that the partic-.

Participants were randomly as-. Studies 1 and 2. Participants were then automatically connected. When participants decided to quit the game by.

First, they were asked to complete 15 items assessing. Next, participants rated their emotional experience during the. Responses were recorded on a.

At the end of the exper-. In order to check the manipulation effectiveness, we computed. The analysis revealed that.

Replicating the findings of Studies 1 and 2, we. We then computed a third t test comparing the two experimental.

The analysis showed that. Thus, imbuing slot machines with human charac-. Finally, we conducted mediational analyses to test for a possible. The manipulation of slot.

Furthermore, the manipulation of slot. Finally, when both positive and negative. Participants were asked whether they had ever played on a slot ma-.

Of the latter group of participants, eight. None reported a higher frequency of slot machine playing. To test for mediation, we employed the bootstrapping method.

This finding suggested that although slot machine anthropomor-. In sum, Study 3 further corroborates our main prediction, show-.

Furthermore, Study 3 explores a mechanism that could account for. Across three studies, we tested the possibility that mind attri-.

We found that when people. We also found that this effect held. Furthermore, this scale included only. The aim of Study 4 was to test the predictions of the current.

To do so, we first conducted an a priori power. Thus, Study 4 tested again the main prediction of the current. Furthermore, Study 4 tested the as-.

Specifically, we expected that the anthropomor-. Finally, using a validated measure of emotions, Study 4.

Again, we expected that the self-reported emotional experi-. Similar to our previous studies, the experiment. Because the online interface of the slot.

The interface represented a slot machine with a. Furthermore, by clicking a specific button, participants could.

In this study, we gave participants an equivalence of candies rather. Similar to Study 2, the winnings were based on the.

The equivalence between the slot machine score and the. The maximum payout was Participants were told that they. After being informed of the correspondence between scores and.

The slot machine interface was loaded on a computer screen and. When participants pressed the. Of the latter group of participants, 53 reported that.

None reported a higher frequency e. Next, similar to previous studies, participants were asked to. Then, participants were asked to report their emo-.

Based on a recent. Finally, participants reported their demographic data i. At the end of the experiment, each participant.

An independent samples t test was first. The analysis revealed that participants anthropo-. Next, we compared the two experimental.

The analysis on the final score participants obtained on the slot. In line with a typical. The two experimental conditions were also compared on the.

Again, we found that participants presented with the anthropomor-. On average, participants in both condi-.

We then computed a series of t tests comparing the two exper-. The analysis showed no significant. By contrast, the analysis showed that.

Next, we examined whether different. We used a bootstrapping procedure Hayes, estimating. By contrast, we found a. According to Hayes , Chapter 6; see also Hayes, ,.

Thus, we estimated indirect effects for all four. When the four emotional clusters were included in the regression.

The analysis provided support for the idea that high-arousal pos-. To test the possibility that our manipulation influenced only the two. Then, we meta-analytically combined the results from the effect.

Therefore, our manipulation affected both the attribution of agency. By contrast, high-arousal negative emotions did not predict.

Similarly, low-arousal positive emotions did not predict gambling. Finally, we conducted another mediation analysis to test the.

The manipulation of slot ma-. The analysis provided support for the mediating role of gam-. In sum, in Study 4 we replicated the main finding.

We also showed that the effects of slot ma-. Then, unlike Study 3, Study 4 did not provide support for the. Finally, Study 4 showed that gambling behavior ac-.

The present research tested the hypothesis that anthropomor-. First, we conducted a preliminary study investigating.

Accordingly, we found that regular players. Specifically, we found that the more people attribute a humanlike.

Then, in Study 1, we manipulated slot machine. In Study 2, we replicated the findings of Study 1. In Study 3, we explored the possible role of self-.

Finally, in Study 4, we extended the effects of. Furthermore, in Study 4, we. The present research substantially extends existing work on the.

A similar indirect mediational pattern of high-arousal positive emo-. Slot machine score Number of candies 3. High-arousal negative emotions 1.

Low-arousal negative emotions 1. High-arousal positive emotions 1. Low-arousal positive emotions 3. Furthermore, the majority of studies have con-.

Many scholars have argued that people have a strong tendency. In our studies, we consistently.

However, the manipulation check means provided by the nonan-. Overall, our findings indicate that even trivial objects such as. Furthermore, they suggest that gambling.

In our studies, the description of slot machines in the. By contrast, the specific. Our research has shown that the consequences of slot machine.

Indeed, we found that people exposed to a humanized. As we noted, this is not.

This finding suggested that although slot machine anthropomor-. In sum, Study 3 further corroborates our main prediction, show-.

Furthermore, Study 3 explores a mechanism that could account for. Across three studies, we tested the possibility that mind attri-. We found that when people.

We also found that this effect held. Furthermore, this scale included only. The aim of Study 4 was to test the predictions of the current.

To do so, we first conducted an a priori power. Thus, Study 4 tested again the main prediction of the current.

Furthermore, Study 4 tested the as-. Specifically, we expected that the anthropomor-. Finally, using a validated measure of emotions, Study 4.

Again, we expected that the self-reported emotional experi-. Similar to our previous studies, the experiment. Because the online interface of the slot.

The interface represented a slot machine with a. Furthermore, by clicking a specific button, participants could. In this study, we gave participants an equivalence of candies rather.

Similar to Study 2, the winnings were based on the. The equivalence between the slot machine score and the.

The maximum payout was Participants were told that they. After being informed of the correspondence between scores and.

The slot machine interface was loaded on a computer screen and. When participants pressed the. Of the latter group of participants, 53 reported that.

None reported a higher frequency e. Next, similar to previous studies, participants were asked to. Then, participants were asked to report their emo-.

Based on a recent. Finally, participants reported their demographic data i. At the end of the experiment, each participant. An independent samples t test was first.

The analysis revealed that participants anthropo-. Next, we compared the two experimental. The analysis on the final score participants obtained on the slot.

In line with a typical. The two experimental conditions were also compared on the. Again, we found that participants presented with the anthropomor-.

On average, participants in both condi-. We then computed a series of t tests comparing the two exper-. The analysis showed no significant. By contrast, the analysis showed that.

Next, we examined whether different. We used a bootstrapping procedure Hayes, estimating. By contrast, we found a. According to Hayes , Chapter 6; see also Hayes, ,.

Thus, we estimated indirect effects for all four. When the four emotional clusters were included in the regression. The analysis provided support for the idea that high-arousal pos-.

To test the possibility that our manipulation influenced only the two. Then, we meta-analytically combined the results from the effect.

Therefore, our manipulation affected both the attribution of agency. By contrast, high-arousal negative emotions did not predict.

Similarly, low-arousal positive emotions did not predict gambling. Finally, we conducted another mediation analysis to test the. The manipulation of slot ma-.

The analysis provided support for the mediating role of gam-. In sum, in Study 4 we replicated the main finding. We also showed that the effects of slot ma-.

Then, unlike Study 3, Study 4 did not provide support for the. Finally, Study 4 showed that gambling behavior ac-. The present research tested the hypothesis that anthropomor-.

First, we conducted a preliminary study investigating. Accordingly, we found that regular players. Specifically, we found that the more people attribute a humanlike.

Then, in Study 1, we manipulated slot machine. In Study 2, we replicated the findings of Study 1. In Study 3, we explored the possible role of self-.

Finally, in Study 4, we extended the effects of. Furthermore, in Study 4, we. The present research substantially extends existing work on the.

A similar indirect mediational pattern of high-arousal positive emo-. Slot machine score Number of candies 3. High-arousal negative emotions 1.

Low-arousal negative emotions 1. High-arousal positive emotions 1. Low-arousal positive emotions 3. Furthermore, the majority of studies have con-.

Many scholars have argued that people have a strong tendency. In our studies, we consistently. However, the manipulation check means provided by the nonan-.

Overall, our findings indicate that even trivial objects such as. Furthermore, they suggest that gambling.

In our studies, the description of slot machines in the. By contrast, the specific. Our research has shown that the consequences of slot machine.

Indeed, we found that people exposed to a humanized. As we noted, this is not. In any case, from a logical standpoint, on.

People primed with a hu-. How does anthropomorphism increase gambling? Study 3 and Study 4, we found that high-arousal positive emotions.

In other words, our participants may. This finding could be in line with previous studies suggesting that. However, whereas in Study 3 we found a direct effect of the.

From an applied standpoint, our research suggests some avenues. We showed that it is pos-. Thus, when the aim is reducing gambling, we recommend framing.

This implies conveying the. By contrast, any form of human-. Accordingly, our studies showed that human-. Again, when the aim is to reduce gambling, anthropo-.

There are some limitations to the present research. Future studies should test. Moreover, future studies should. Second, future research should also consider other potential.

As we have already noted, re-. This reason could account for the effect. In addition to increasing a sense of efficacy, anthropomorphism.

Magical thinking, expectations of winning, feelings of competi-. Thus, future research should further investigate the role.

Finally, future research should also consider the possible individual. For instance, it could be tested whether people with an. In this sense, the ques-.

Slot machines are designed to induce people to play more and to. As our data suggest, one of the simplest ways to reduce. When good brands do bad.

Journal of Consumer Research, 31, 1— Social Robotics, 1, 71— The need to belong: The existential theory of mind. Mangy mutt or furry.

Anthropomorphism promotes animal welfare. Shared initials increase disaster donations. Judgment and Decision Mak-.

Use does not wear ragged the fabric. Thinking of objects as alive makes people less willing to. Journal of Consumer Psychology, 20, — Contemporary Hospitality Management, 25, 23— The Journal of Psychology,.

Motivational determinants of anthropomorphism. Social categorization of social. Anthropomorphism as a function of robot group membership.

British Journal of Social Psychology, 51, — Behavior Research Methods, 39, — Love makes you real: Social Cognition, 26, — The role of subjective mood states in the maintenance.

Journal of Gambling Studies, 11,. Betting your life on it. British Medical Journal, ,. The role of cognitive bias and skill in fruit machine.

British Journal of Psychology, 85, — Journal of Marketing Management, 29, — Beyond Baron and Kenny: Communication Monographs, 76, —.

Introduction to mediation, moderation, and condi-. Slot or gaming the slot. Power, anthropomorphism, and risk perception.

Consumer Research, 38, 94 — What do I think. Action identification and mind attribution. Personality and Social Psychology, 90, — Emotional arousal and memory binding: Perspectives on Psychological Science, 2, 33— Journal of Social Issues, 56, 81— Are machines gender neutral?

Gender-stereotypic responses to computers with voices. Reminders of death and anthropomorphizing nature. Retrieved August 16, , from http: An integrative approach to affective neuroscience, cognitive.

Behavior Research Methods, 40, — When Mother Earth rises up:. Anthropomorphizing nature reduces support for natural disaster victims. Social Psychology, 44, — Toward an explication of attachment as a consumer.

Advances in Consumer Research, 16, — VLT gambling in Alberta: Alberta Gambling Research Institute. Anthropomorphism enhances connectedness to and protectiveness to-.

Journal of Experimental Social Psychology, 49, — How do slot machines and other elec-. Journal of Gambling Issues,. Irrational thinking among slot machine players.

Journal of Gambling Studies, 8, — Journal of Marketing, 71, — Manual for the positive. Perspectives on Psychological Science, 5, — Making sense by making sentient: Journal of Personality and.

Social Psychology, 99, — Two motivations for two dimensions of. Journal of Experimental Social Psychology, 55, — Transitional objects and transitional phenomena;.

The International Journal of. Psychoanalysis, 34, 89 — Towards a constructionist ap-. Verification of the three-dimensional model of. Donating to disaster victims: Responses to natural and humanly.

European Journal of Social Psychology, 41, — The slot machine acts according to its own intentions. The slot machine has consciousness. The slot machine has free will.

The slot machine perceives stimuli. The slot machine experiences emotions. We use cookies to make interactions with our website easy and meaningful, to better understand the use of our services, and to tailor advertising.

For further information, including about cookie settings, please read our Cookie Policy. By continuing to use this site, you consent to the use of cookies.

Data quantization methods for continuous attributes play an extremely important role in artificial intelligence, data mining and machine learning because discrete values of attributes are required in most classification methods.

In this paper, we present a supervised statistical data quantization method. It defines a quantization criterion based on the chi-square statistic to discover accurate merging intervals.

In addition, a heuristic quantization algorithm is proposed to achieve a satisfying quantization result with the aim to improve the performance of inductive learning algorithms.

Empirical experiments on UCI real data sets show that our proposed algorithm generates a better quantization scheme that improves the classification accuracy of C4.

Taxonomy of recent discretization techniques. Following references are cited in artwork [1,6,16,17,18,21,30,34,35,37,[42] [43] [44][45]48].

In recent years, imbalanced dataset classification has received significant attention due to its application in real-world problems, resulting in emergence of a new class of algorithms.

Classification algorithms that work with discretized data have been shown to yield better performance. Thus, discretization is often a critical technique in data preprocessing.

The proposed algorithm takes advantage of evolutionary multi-objective optimization to simultaneously optimize three objective functions: The first objective function uses AUC, instead of classification accuracy, to choose better cut points so as to identify the minority class.

The second objective function reduces the number of cut points while in the third objective function, low-frequency cut points are selected so that information loss caused by continuous to discrete data discretization is minimized.

To evaluate the proposed algorithm, 25 imbalanced benchmark datasets are totally used and the results are compared to those of popular algorithms in the literature such as Class-Attribute Interdependence Maximization CAIM and Evolutionary Multi-objective Discretization EMD.

Our findings indicate that the proposed algorithm outperforms the other techniques in terms of the number of cut points, AUC, and non-parametric statistical tests.

Rough-set classifier based on discretization for breast cancer diagnosis. Breast cancer is a kind of common malignant tumor of women.

It is becoming a leading cause of death among women. But the early detection and diagnosis of this disease can ensure a long survival of patients.

Classification plays an increasingly important role in machine learning and data mining. A rough-set classifier based on discretization RSCBD is proposed in this paper for breast cancer diagnosis.

It is built on fully considering the significance of condition attributes, classification attributes and attribute thresholds.

Experiment results prove the RSCBD can get higher classification accuracy, lower reject rate, breakpoints and rules, which are important for breast cancer diagnosis.

Rough set-based approaches for discretization: The extraction of knowledge from a huge volume of data using rough set methods requires the transformation of continuous value attributes to discrete intervals.

This paper presents a systematic study of the rough set-based discretization RSBD techniques found in the literature and categorizes them into a taxonomy.

In the literature, no review is solely based on RSBD. Only a few rough set discretizers have been studied, while many new developments have been overlooked and need to be highlighted.

Therefore, this study presents a formal taxonomy that provides a useful roadmap for new researchers in the area of RSBD.

The review also elaborates the process of RSBD with the help of a case study. The study of the existing literature focuses on the techniques adapted in each article, the comparison of these with other similar approaches, the number of discrete intervals they produce as output, their effects on classification and the application of these techniques in a domain.

The techniques adopted in each article have been considered as the foundation for the taxonomy. Moreover, a detailed analysis of the existing discretization techniques has been conducted while keeping the concept of RSBD applications in mind.

The findings are summarized and presented in this paper. We investigate in this paper approximate operations on sets, approximate equality of sets, and approximate inclusion of sets.

The presented approach may be considered as an alternative to fuzzy sets theory and tolerance theory. Some applications are outlined.

A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks.

Its main goal is to transform a set of continuous attributes into discrete ones. The literature provides numerous proposals of discretization and some attempts to categorize them into a taxonomy can be found.

However, in previous papers, there is a lack of consensus in the definition of the properties and no formal categorization has been established yet, which may be confusing for practitioners.

Furthermore, only a small set of discretizers have been widely considered, while many other methods have gone unnoticed. This paper provides a survey of discretization methods proposed in the literature from a theoretical and empirical perspective.

We develop a taxonomy based on the main properties pointed out in previous research, unifying the notation and including all the known methods up to date.

Empirically, we conduct an experimental study in supervised classification involving the most representative and newest discretizers, different types of classifiers and a large number of data sets.

The results of their performances have been verified by means of nonparametric statistical tests.

Additionally, a set of discretizers are highlighted as the best performing ones. Discretization, defined as a set of cuts over domains of attributes, represents an important pre-processing task for numeric data analysis.

Some Machine Learning algorithms require a discrete feature space but in real-world applications continuous attributes must be handled.

To deal with this problem many supervised discretization methods have been proposed but little has been done to synthesize unsupervised discretization methods to be used in domains where no class information is available.

Furthermore, existing methods such as equal-width or equal-frequency binning, are not well-principled, raising therefore the need for more sophisticated methods for the unsupervised discretization of continuous features.

This paper presents a novel unsupervised discretization method that uses non-parametric density estimators to automatically adapt sub-interval dimensions to the data.

The proposed algorithm searches for the next two sub-intervals to produce, evaluating the best cut-point on the basis of the density induced in the sub-intervals by the current cut and the density given by a kernel density estimator for each sub-interval.

It uses cross-validated log-likelihood to select the maximal number of intervals.

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