Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral

Tehrani, Ali Fallah; Cheng, Weiwei; Hüllermeier, Eyke

doi:10.2991/eusflat.2011.86

Cited by 16 publications

(3 citation statements)

References 22 publications

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“…• Choquistic Regression [64,16,65]. The basic idea of choquistic regression is to replace the linear function of predictor variables, which is commonly used in logistic regression to model the log odds of the positive class, by the choquet integral [66].…”

Section: Fuzzy Integralsmentioning

confidence: 99%

Monotonic classification: An overview on algorithms, performance measures and data sets

et al. 2019

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Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently,

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Section: Fuzzy Integralsmentioning

confidence: 99%

Monotonic classification: An overview on algorithms, performance measures and data sets

et al. 2019

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“…In the literature, some monotone classifiers have been proposed [2,3,7,6,18,11,27] but, as shown in [4], they deeply suffer from non-monotone noise present in the data and in many cases do not have classification accuracy as primary goal. Moreover, in order to ensure the monotonicity of the final classifier λ , a monotonization phase of the initial dataset or of the final classifier could be necessary [10,25], causing a possible loss of information.…”

Section: Introductionmentioning

confidence: 99%

Monotone Classification with Decision Trees

Marsala

Petturiti²

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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In machine learning, monotone classification is concerned with a classification function to learn in order to guarantee a kind of monotonicity of the class with respect to attribute values. In this paper, we focus on rank discrimination measures to be used in decision tree induction, i.e., functions able to measure the discrimination power of an attribute with respect to the class taking into account the monotonicity of the class with respect to the attribute. Three new measures are studied in detail and an experimental analysis is also provided, comparing the proposed approach with other well-known monotone and non-monotone classifiers in terms of classification accuracy

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“…This can be done in different ways. In [15], for example, the authors propose a model that can be seen as an extension of logistic regression. The basic idea of this approach is to model the log-odds ratio between the positive (y = 1) and the negative (y = 0) class as a function of the Choquet integral of the input attributes.…”

Section: The Choquet Integral As a Tool For Classificationmentioning

confidence: 99%

On the VC-Dimension of the Choquet Integral

Hüllermeier

Tehrani

2012

Communications in Computer and Information Science

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Abstract. The idea of using the Choquet integral as an aggregation operator in machine learning has gained increasing attention in recent years, and a number of corresponding methods have already been proposed. Complementing these contributions from a more theoretical perspective, this paper addresses the following question: What is the VC dimension of the (discrete) Choquet integral when being used as a binary classifier? The VC dimension is a key notion in statistical learning theory and plays an important role in estimating the generalization performance of a learning method. Although we cannot answer the above question exactly, we provide a first interesting result in the form of (relatively tight) lower and upper bounds.

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Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral

Cited by 16 publications

References 22 publications

Monotonic classification: An overview on algorithms, performance measures and data sets

Monotonic classification: An overview on algorithms, performance measures and data sets

Monotone Classification with Decision Trees

On the VC-Dimension of the Choquet Integral

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