Earticle

초록

영어

The motivation of this paper is based on a hypothesis that non-linear decision nodes provide a better classification performance than axis-parallel decision nodes do in many practical problems, such as image classification, and voice classification. The algorithm – MNCS_DT is introduced in this paper to create non-linear splits nodes by novel discriminant analysis in decision tree for multi-classification problem and take cost-sensitive problem into account when the features are selected. In experiment part, we use four UCI data sets to compare the performance of MNCS_DT and C4.5 CS by costs and error rates. The performance of MNCS_DT is better than C4.5 CS. And eight data sets from UCI are used to compare the performance of three different feature sets measured by accuracy, G-mean, and operation time. The performance of feature set consisting of features that follow multivariate normal distribution and altered information gain values higher than average one is better than two other feature sets in most data sets.

목차

Abstract
 1. Introduction
 2. Cost-sensitive Nonlinear Decision Tree Algorithm
 3. MNCS_DT
  3.1 Altered Information Gain Ratio
  3.2 Non-linear Discriminant Analysis
 4. Experience
  4.1 Data Sets Description
  4.2 Performance of MNCS_DT Compared with C4.5CS
  4.3 Performance of MNCS_DT with Different Feature Sets
 5. Conclusion
 References

키워드

저자

• Weiwei Duan [ University of Southern California, CA, 90007 University of Science and Technology of China, Anhui, 230011 ]
• Cheng Ding [ University of Southern California, CA, 90007 University of Science and Technology of China, Anhui, 230011 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605