Classification trees for problems with monotonicity constraints

Potharst, Rob; Feelders, Ad

doi:10.1145/568574.568577

Cited by 109 publications

(61 citation statements)

References 10 publications

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“…With the help of monotonicity generating classification trees that satisfies the constraint property, both when training data which satisfies monotone and when it is not. [1] ii. Constraint frequent pattern mining with a patter growth view finds all frequent itemset that satisfy the constraint and then the pattern growth mining method generates and test only a few among them.…”

Section: Constraint Based Approachesmentioning

confidence: 99%

Survey on Constrained based Data Stream Mining

Kurien¹,

Sreekumar²,

Kk³

2014

IJCA

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In most of the real time applications data may arrive as continuous ordered sequence of items, called Data streams. The main challenge in dealing with the Data Stream is its voluminous, complex and dynamically arriving stream of data. There are certain techniques to deal with data streams, in particular, finding the frequent or sequential patterns that occur repeatedly. These results retrieve huge number of patterns, which are hard to analyze and use them, also difficult to store these results and its intermediate results. The traditional pattern mining techniques fail to give the relevant details to the user. In order to obtain that, some constraint based mining techniques, which acts as a filter to the large result set retrieved from traditional pattern mining techniques. This paper investigates different data stream mining techniques and constrained based stream mining techniques from which the user gets the required information from the data stream. General TermsData Stream, Pattern.

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Section: Constraint Based Approachesmentioning

confidence: 99%

Survey on Constrained based Data Stream Mining

Kurien¹,

Sreekumar²,

Kk³

2014

IJCA

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“…Meanwhile, several machine learning algorithms have been modified so as to guarantee monotonicity in attributes, including nearest neighbor classification [15], decision tree learning [16] and rule induction [17]. Instead of modifying models and algorithms, one can also modify the data.…”

Section: Related Workmentioning

confidence: 99%

Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral

Tehrani¹,

Cheng²,

Hüllermeier³

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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In this paper, we propose a generalization of logistic regression based on the Choquet integral. The basic idea of our approach, referred to as 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. Thus, it becomes possible to capture non-linear dependencies and interactions among predictor variables while preserving two important properties of logistic regression, namely the comprehensibility of the model and the possibility to ensure its monotonicity in individual predictors. In experimental studies with real and benchmark data, choquistic regression consistently improves upon standard logistic regression in terms of predictive accuracy.

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“…Most notably, it combines three features in a non-trivial way, namely monotonicity, nonlinearity and interpretability. As for the first, a monotone dependence between the input and output attributes is often desirable in a classification setting and sometimes even requested by the application [11,12,13]. At the same time, the Choquet integral also allows for modeling interactions between different attributes in a flexible, nonlinear way.…”

Section: Introductionmentioning

confidence: 99%

Efficient Learning of Classifiers Based on the 2-Additive Choquet Integral

Hüllermeier

Tehrani

2013

Studies in Computational Intelligence

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Abstract. In a recent work, we proposed a generalization of logistic regression based on the Choquet integral. Our approach, referred to as choquistic regression, makes it possible to capture non-linear dependencies and interactions among predictor variables while preserving two important properties of logistic regression, namely the comprehensibility of the model and the possibility to ensure its monotonicity in individual predictors. Unsurprisingly, these benefits come at the expense of an increased computational complexity of the underlying maximum likelihood estimation. In this paper, we propose two approaches for reducing this complexity in the specific though practically relevant case of the 2-additive Choquet integral. Apart from theoretical results, we also present an experimental study in which we compare the two variants with the original implementation of choquistic regression.

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Classification trees for problems with monotonicity constraints

Cited by 109 publications

References 10 publications

Survey on Constrained based Data Stream Mining

Survey on Constrained based Data Stream Mining

Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral

Efficient Learning of Classifiers Based on the 2-Additive Choquet Integral

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