A Robust Algorithm for Classification Using Decision Trees

Author

Chandra, B. ; Paul V, P.

Author_Institution

Dept. of Math., IIT, New Delhi

fYear

2006

fDate

7-9 June 2006

Firstpage

1

Lastpage

5

Abstract

Decision trees algorithms have been suggested in the past for classification of numeric as well as categorical attributes. SLIQ algorithm was proposed (Mehta et al., 1996) as an improvement over ID3 and C4.5 algorithms (Quinlan, 1993). Elegant Decision Tree Algorithm was proposed (Chandra et al. 2002) to improve the performance of SLIQ. In this paper a novel approach has been presented for the choice of split value of attributes. The issue of reducing the number of split points has been addressed. It has been shown on various datasets taken from UCI machine learning data repository that this approach gives better classification accuracy as compared to C4.5, SLIQ and Elegant Decision Tree Algorithm (EDTA) and at the same time the number of split points to be evaluated is much less compared to that of SLIQ and EDTA

Keywords

decision trees; pattern classification; Elegant Decision Tree Algorithm; SLIQ algorithm; UCI machine learning data repository; classification algorithm; decision tree algorithms; decision tree classifier; split point reduction; Classification algorithms; Classification tree analysis; Computational complexity; Decision trees; Entropy; Gain measurement; Machine learning; Machine learning algorithms; Mathematics; Robustness; Classification; Gain Ratio; Gini Index; Information gain;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetics and Intelligent Systems, 2006 IEEE Conference on

Conference_Location

Bangkok

Print_ISBN

1-4244-0023-6

Type

conf

DOI

10.1109/ICCIS.2006.252336

Filename

4017895