关键词: C4.5算法, 二义性数据, Fleiss&rsquo, Kappa系数, 朴素贝叶斯算法

Abstract:

The problem of the decision tree C4.5 algorithm contains the instability of dealing with petty missing data and ambiguous data, and the problem of handling the relationship between conditional attributes when splitting nodes as well. Focusing on the problems above, an improved algorithm of Fleiss’ Kappa coefficient is introduced, based on the combination of decision tree C4.5 algorithm and naive Bayes algorithm. It can solve the problem more effectively and precisely which the C4. 5 algorithm cannot deal. According to the theoretical analysis and experimental results based on the standard UCI data sets, the classification accuracy is significantly improved with the price of sacrificing certain execution efficiency.

Key words: C4. 5 algorithm, ambiguous data, Fleiss&rsquo, Kappa coefficient, naive Bayesian algorithm