A New Method of Improving Classification Accuracy of Decision Tree in Case of Incomplete Samples

Nowak, B.; Nowicki, Robert; Mleczko, Wojciech K.

doi:10.1007/978-3-642-38658-9_40

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…Complexity of a decision tree increases by higher number of features. Although only a few features have been observed in certain situations to be capable of determining the belonging class of an object, remaining features have weak or no effect (Nowak et al, 2013). This approach is one of the nonparametric classification methods that may be classified into two groups with regard to type of dependent variable: tree sorting for discrete variable and variable batch, and tree regression for continuous variable.…”

Section: Decision Treementioning

confidence: 99%

A novel hybrid support vector machine with decision tree for data classification

Gashti

2017

Int. j. adv. appl. sci.

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The purpose of this paper is to increase the accuracy of a proposed support vector machine model using hybrid model of SVM and ID3. Then the hybrid approach based on SVM and ID3 tree will be evaluated focusing on analyzing the impact of ID3 on SVM performance. The evaluation process was carried out on the global dataset and Adult reference extracted from KEEL dataset repository. The obtained results demonstrate higher classification accuracy (0.9125) of the proposed model compared to SVM and ID3.

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Section: Decision Treementioning

confidence: 99%

A novel hybrid support vector machine with decision tree for data classification

Gashti

2017

Int. j. adv. appl. sci.

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“…The considered problem with the application of Hoeffding's inequality was also noticed by other scientists, e.g. in [21], [13] or [22].…”

Section: Introductionmentioning

confidence: 96%

A novel application of Hoeffding's inequality to decision trees construction for data streams

Duda

Jaworski

Pietruczuk

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Decision trees are the commonly applied tools in the task of data stream classification. The most critical point in decision tree construction algorithm is the choice of the splitting attribute. In majority of algorithms existing in literature the splitting criterion is based on statistical bounds derived for split measure functions. In this paper we propose a totally new kind of splitting criterion. We derive statistical bounds for arguments of split measure function instead of deriving it for split measure function itself. This approach allows us to properly use the Hoeffding's inequality to obtain the required bounds. Based on this theoretical results we propose the Decision Trees based on the Fractions Approximation algorithm (DTFA). The algorithm exhibits satisfactory results of classification accuracy in numerical experiments. It is also compared with other existing in literature methods, demonstrating noticeably better performance.

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Nowicki¹

2018

Studies in Computational Intelligence

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A New Method of Improving Classification Accuracy of Decision Tree in Case of Incomplete Samples

Cited by 8 publications

References 28 publications

A novel hybrid support vector machine with decision tree for data classification

A novel hybrid support vector machine with decision tree for data classification

A novel application of Hoeffding's inequality to decision trees construction for data streams

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