Designing decision trees with the use of fuzzy granulation

Pedrycz, Witold; Sosnowski, Zenon A.

doi:10.1109/3468.833095

Cited by 48 publications

(17 citation statements)

References 9 publications

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“…[56,36]) and seem to remain a topic of interest even today [47][48][49] (see [44] for a recent approach and a comprehensive overview of research in this field). In fact, these approaches provide a typical example for the "fuzzification" of standard machine learning methods.…”

Section: Fuzzy Decision Tree Inductionmentioning

confidence: 99%

Fuzzy sets in machine learning and data mining

Hüllermeier

2011

Applied Soft Computing

100

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Machine learning, data mining, and several related research areas are concerned with methods for the automated induction of models and the extraction of interesting patterns from empirical data. Automated knowledge acquisition of that kind has been an essential aspect of artificial intelligence since a long time and has more recently also attracted considerable attention in the fuzzy sets community. This paper briefly reviews some typical applications and highlights potential contributions that fuzzy set theory can make to machine learning, data mining, and related fields. In this connection, some advantages of fuzzy methods for representing and mining vague patterns in data are especially emphasized.

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Section: Fuzzy Decision Tree Inductionmentioning

confidence: 99%

Fuzzy sets in machine learning and data mining

Hüllermeier

2011

Applied Soft Computing

100

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“…Entropy (10) This is an amalgamation of the two forms of fuzzy entropy, the first term on the right corresponding to (9) and the second term relating to the fuzzy entropy part of (8).…”

Section: Case Dmentioning

confidence: 99%

“…Xizhao and Hong [7] discretize continuous attributes using fuzzy numbers and possibility theory. Pedrycz and Sosnowski [8], on the other hand, employ context-based fuzzy clustering for this purpose. Yuan and Shaw [9] induce a fuzzy decision tree by reducing classification ambiguity with fuzzy evidence.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation

Mitra

Konwar

Pal

2002

IEEE Trans. Syst., Man, Cybern. C

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Abstract-A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy decision tree. A scheme for automatic linguistic discretization of continuous attributes, based on quantiles, is formulated. A novel concept for measuring the goodness of a decision tree, in terms of its compactness (size) and efficient performance, is introduced. Linguistic rules are quantitatively evaluated using new indices. The rules are mapped to a fuzzy knowledge-based network, incorporating the frequency of samples and depth of the attributes in the decision tree. New fuzziness measures, in terms of class memberships, are used at the node level of the tree to take care of overlapping classes. The effectiveness of the system, in terms of recognition scores, structure of decision tree, performance of rules, and network size, is extensively demonstrated on three sets of real-life data.

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“…Fuzzy variants of decision tree induction have been developed for quite a while (e.g [29,14,17,18]) and seem to remain a topic of interest even today [20,21,22]. We recommend [19] as a one of the most sophisticated approaches, including a comprehensive overview of research in this field.…”

Section: Fuzzy Decision Treesmentioning

confidence: 99%

Why Fuzzy Decision Trees are Good Rankers

Hüllermeier

Vanderlooy

2009

IEEE Trans. Fuzzy Syst.

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Several fuzzy extensions of decision tree induction, an established machine learning method, have already been proposed in the literature. So far, however, fuzzy decision trees have almost exclusively been used for the performance task of classification. In this paper, we show that a fuzzy extension of decision trees is arguably more useful for another performance task, namely ranking. Roughly, the goal of ranking is to order a set of instances from most likely positive to most likely negative. The motivation for applying fuzzy decision trees to this problem originates from recent investigations of the ranking performance of conventional decision trees. These investigations will be continued and complemented in this paper. Our results reveal some properties which seem to be crucial for a good ranking performance-properties that are better and more naturally offered by fuzzy than by conventional decision trees. Most notably, a fuzzy decision tree produces scores in terms of membership degrees on a fine-granular scale. Using these membership degrees as a ranking criterion, a key problem of conventional decision trees is solved in an elegant way, namely the question of how to break ties between instances in the same leaf or, more generally, between equally-scored instances.

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Designing decision trees with the use of fuzzy granulation

Cited by 48 publications

References 9 publications

Fuzzy sets in machine learning and data mining

Fuzzy sets in machine learning and data mining

Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation

Why Fuzzy Decision Trees are Good Rankers

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