Experiments on Classifiers Obtained Via Decision Tree Induction Methods with Different Attribute Acquisition Cost Limit

Penar, Wojciech; Woźniak, Michał

doi:10.1007/978-3-540-75175-5_47

Cited by 8 publications

(2 citation statements)

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“…We use the proposition known as EG2 (Nu´nez, 1991) with different values instead of the information gain function to determine which attribute has to be chosen for node creation (Penar & Wozniak, 2007). First, we add the following input parameters:…”

Section: Propositions Of the New Methodsmentioning

confidence: 99%

Cost‐sensitive methods of constructing hierarchical classifiers

Penar

Woźniak

2010

Expert Systems

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Abstract:The cost of a future exploitation of a decision support system plays a key role. The paper deals with the problem of feature value acquisition cost for such systems. We present a modification of a cost-sensitive learning method for decision-tree induction with fixed attribute acquisition cost limit. Properties of the concept are established during computer experiments conducted on chosen benchmark databases from the UCI Machine Learning Repository and a real medical decision task. The results of experiments confirm that, for some decision problems, our proposition allows us to obtain a classifier with the same quality as a classifier obtained without cost limit but its exploitation is cheaper.

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Section: Propositions Of the New Methodsmentioning

confidence: 99%

Cost‐sensitive methods of constructing hierarchical classifiers

Penar

Woźniak

2010

Expert Systems

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“…However, a multistage (sequential) approach breaks up a complex decision into a collection of several simpler decisions [1][2][3][4]. Many algorithm build a tree structure in the learning process [5,6]. In other approaches the decision tree structure is fixed before the learning process [7].…”

Section: Introductionmentioning

confidence: 99%

Construction of Sequential Classifier Using Confusion Matrix

Burduk

Trajdos

2013

Computer Information Systems and Industrial Management

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Part 7: AlgorithmsInternational audienceThis paper presents the problem of building the decision scheme in the multistage pattern recognition task. This task can be presented using a decision tree. This decision tree is built in the learning phase of classification. This paper proposes a split criterion based on the analysis of the confusion matrix. Specifically, we propose the division associated with an incorrect classification. The obtained results were verified on the data sets form UCI Machine Learning Repository and one real-life data set of the computer-aided medical diagnosis

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The Method of Improving the Structure of the Decision Tree Given by the Experts

Burduk

2013

Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013

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Experiments on Classifiers Obtained Via Decision Tree Induction Methods with Different Attribute Acquisition Cost Limit

Cited by 8 publications

References 5 publications

Cost‐sensitive methods of constructing hierarchical classifiers

Cost‐sensitive methods of constructing hierarchical classifiers

Construction of Sequential Classifier Using Confusion Matrix

The Method of Improving the Structure of the Decision Tree Given by the Experts

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