Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems

Umanol, M.; Okamoto, Hitoshi; Hatono, Itsuo; Tamura, Hiroyuki; Kawachi, F.; Umedzu, S.; Kinoshita, J.

doi:10.1109/fuzzy.1994.343539

Cited by 155 publications

(102 citation statements)

References 2 publications

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“…Finally, we modify the G-FDT by substituting our proposed method of constructing discriminators for the one of G-FDT proposed by B. Chandra, and the fuzzy inference mechanism of our proposed algorithm is the same as the one used in [1], so called "×-×-+ method" [12]. Thus, the version of modified G-FDT is our proposed fuzzy decision tree algorithm called sensitivity degree based fuzzy SLIQ decision tree algorithm (called SG-FDT for short).…”

Section: ⅲ Sensitivity Degree Based Induction Algorithmmentioning

confidence: 99%

“…With the seminal work of Zadeh, fuzzy set theory [15] has a valuable extension to traditional crisp decision trees and the fuzzy counterparts of traditional heuristics of induction of crisp trees have been proposed [12] [24]. Fuzzy decision trees induced by the fuzzified heuristics well process the data that cognitive uncertainties, such as vagueness and ambiguity, are incorporated into.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy SLIQ Decision Tree Based on Classification Sensitivity

Qiu

Zhang²

2011

IJMECS

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Abstract-The determination of membership function is fairly critical to fuzzy decision tree induction. Unfortunately, generally used heuristics, such as SLIQ, show the pathological behavior of the attribute tests at split nodes inclining to select a crisp partition. Hence, for induction of binary fuzzy tree, this paper proposes a method depending on the sensitivity degree of attributes to all classes of training examples to determine the transition region of membership function. The method, properly using the pathological characteristic of common heuristics, overcomes drawbacks of G-FDT algorithm proposed by B. Chandra, and it well remedies defects brought on by the pathological behavior. Moreover, the sensitivity degree based algorithm outperforms G-FDT algorithm in respect to classification accuracy.

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Section: ⅲ Sensitivity Degree Based Induction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy SLIQ Decision Tree Based on Classification Sensitivity

Qiu

Zhang²

2011

IJMECS

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“…This application can be executed on the top of Globus toolkit middleware. In this architecture, first an FDT is constructed using the FDT Algorithm [14], [15]. This algorithm is a developed version of ID3 [16] that operates on fuzzy sets and produces an FDT.…”

Section: A Fdt Based Resource Managementmentioning

confidence: 99%

A Survey on Application of Machine Learning to Resource Management in Grid Environment

Singh¹,

Sarkar²,

Roy³

et al. 2013

LNSE

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Abstract-Grid represents an environment over a distributed area, incorporating heterogeneous elements such as server nodes, storage devices, and network components in a scalable, wide-area spanning compute infrastructure. Since a Grid requires large scale resource sharing, efficient resource management system (RMS) is required to manage the Quality of Service (QoS). One of the chief tasks of an RMS for Grid is selecting an appropriate and most suitable resource provider for execution of a particular job submitted by a user. This is also known as resource brokering. This paper presents a survey of resource brokering paradigms based on Machine Learning (ML). Three major ML techniques applied in Grid resource management for this purpose have been discussed.

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“…But they cannot easily take into account small changes in the input case, since the predicted class (or predicted probability) is a combination of partial results. Fuzzy DT ( [12]) try also to take into account the possible fluctuations of the breakpoint value of the tests, that are calculated on a learning sample. This can be done by defining a fuzzy area around the hyperplane supporting a test with membership functions (see [13] for an example).…”

Section: Related Workmentioning

confidence: 99%

Sensitivity Analysis of the Result in Binary Decision Trees

Alvarez¹

2004

Machine Learning: ECML 2004

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Abstract. This paper proposes a new method to qualify the result given by a decision tree when it is used as a decision aid system. When the data are numerical, we compute the distance of a case from the decision surface. This distance measures the sensitivity of the result to a change in the input data. With a different distance it is also possible to measure the sensitivity of the result to small changes in the tree. The distance from the decision surface can also be combined to the error rate in order to provide a context-dependent information to the end-user.

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Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems

Cited by 155 publications

References 2 publications

Fuzzy SLIQ Decision Tree Based on Classification Sensitivity

Fuzzy SLIQ Decision Tree Based on Classification Sensitivity

A Survey on Application of Machine Learning to Resource Management in Grid Environment

Sensitivity Analysis of the Result in Binary Decision Trees

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