Customer’s class transformation for profit maximization in multi-class setting of Telecom industry using probability estimation decision trees

Muneiah, Janapati Naga; Rao, Ch. D. V. Subba

doi:10.3233/jifs-190628

Cited by 1 publication

(1 citation statement)

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“…When the user needs a simple and interpretable classifier, DT is the most preferable one. For the automatic extraction of actionable knowledge, many researchers have considered the DT as the model [3,4,5]. In general, classification through DTs comprises three phases namely, training phase, testing phase, and classification phase.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree

Muneiah

Rao

2020

ELCVIA

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So far, most of the research on classification algorithms in machine learning has been focused only on improving the training speed and further improving the technical performance evaluation measures of the constructed models. There is no focus on improving the runtime efficiency of the classification phase which is much required in some critical applications. In this paper, we are considering the computation complexity of a decision tree's classification phase as the major criterion. A novel approach has been proposed to predict the class label of an unseen instance using the decision tree in less time than the regular tree traversal method. In the proposed method, the constructed decision tree is represented in the form of arrays. Then, the process of finding the class label is carried out by performing the bitwise operations between the elements of the arrays and test instance. Empirical results on various UCI data sets proved that the proposed method outperforms the standard method and five other benchmark classifiers and its classification is at least four times faster than the regular method.

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Section: Introductionmentioning

confidence: 99%