Foundations of Rule Learning

Abstract:A proposal for a new method of classification of objects of various nature, named "2"-soft classification, which allows for referring objects to one of two types with optimal entropy probability for available collection of learning data with consideration of additive errors therein. A decision rule of randomized parameters and probability density function (PDF) is formed, which is determined by the solution of the problem of the functional entropy linear programming. A procedure for "2"-soft classification is developed, consisting of the computer simulation of the randomized decision rule with optimal entropy PDF parameters. Examples are provided.

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“…In the experimental study, we used a model that coincides by its structure with (15) and (16), but has randomized parameters and noises:…”

Section: Randomized Model (Decision Rule)mentioning

confidence: 99%

“…Relevant references to them can be found in monographs [1][2][3][4][5][6][7][8], lectures [9,10] and reviews [11][12][13]. The recent fundamental works [6,14,15] clarify the vast diversity of classification algorithms and its learning procedures.…”

Section: Introductionmentioning

confidence: 99%

Entropy “2”-Soft Classification of Objects

Popkov

Volkovich

Dubnov

et al. 2017

Entropy

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“…The SD approach hence relies on a quality measure which evaluates the singularity of the subgroup within the population regarding a target class function: the class attribute. The choice of the measure depends on the dataset but also on the purpose of the application [8]. There are two main kind of quality measures: the first one is used with monolabeled dataset, e.g., the F-1 measure, the WRAcc measure, the Giny index or the entropy (the original SD [17]) ; and the second one is used with multilabeled dataset, e.g., the Weighted Kullback-Leibler divergence(WKL) as used in EMM [6]).…”

Section: Subgroup Discoverymentioning

confidence: 99%

“…The original F-Score. Complete surveys help understanding how to choose the right measure [8]. The generalized version of the WKL 1 considers the labels in the subset L ⊆ Dom(C) as independent and does not look for their cooccurrences.…”

Section: Redescription Miningmentioning

confidence: 99%

Local Subgroup Discovery for Eliciting and Understanding New Structure-Odor Relationships

et al. 2016

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Abstract. From a molecule to the brain perception, olfaction is a complex phenomenon that remains to be fully understood in neuroscience. A challenge is to establish comprehensive rules between the physicochemical properties of the molecules (e.g., weight, atom counts) and specific and small subsets of olfactory qualities (e.g., fruity, woody). This problem is particularly difficult as the current knowledge states that molecular properties only account for 30% of the identity of an odor: predictive models are found lacking in providing universal rules. However, descriptive approaches enable to elicit local hypotheses, validated by domain experts, to understand the olfactory percept. Based on a new quality measure tailored for multi-labeled data with skewed distributions, our approach extracts the top-k unredundant subgroups interpreted as descriptive rules description → {subset of labels}. Our experiments on benchmark and olfaction datasets demonstrate the capabilities of our approach with direct applications for the perfume and flavor industries.

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“…The results of rule-based methods may show closer resemblance to the reasoning of psychologists working in applied settings, and allow for direct identification of high-and/or low-risk patients. Due to their interpretability and flexibility, rulebased methods have already gained popularity in the areas of machine learning and data mining (Fürnkranz, Gamberger, & Lavrač, 2012). Furthermore, the decision rules resulting from the application of rule-based methods can be represented as fast and frugal trees (FFTs; Martignon, Vitouch, Takezawa, & Forster, 2003): graphically represented decision tools, developed within the area of heuristical decision making (Gigerenzer & Goldstein, 1996;Gigerenzer, Todd, & the ABC Research Group, 1999).…”

Section: Introductionmentioning

confidence: 99%

Connecting clinical and actuarial prediction with rule-based methods.

Fokkema¹,

Smits²,

Kelderman³

et al. 2015

Psychological Assessment

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Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. In this paper, we argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic, as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of two simple decision rules, requiring evaluation of only two to four cues. Predictive accuracy of the two-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the two-rule model required evaluation of only three cues, on average. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods.

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Foundations of Rule Learning

Cited by 300 publications

References 29 publications

Entropy “2”-Soft Classification of Objects

Entropy “2”-Soft Classification of Objects

Local Subgroup Discovery for Eliciting and Understanding New Structure-Odor Relationships

Connecting clinical and actuarial prediction with rule-based methods.

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