Using Rules to Analyse Bio-medical Data: A Comparison between C4.5 and PCL

摘要

For easy comprehensibility, rules are preferrable to non-linear kernel functions in the analysis of bio-medical data. In this paper, we describe two rule induction approaches-C4.5 and our PCL classifier-for discovering rules from both traditional clinical data and recent gene expression or proteomic profiling data. C4.5 is a widely used method, but it has two weaknesses, the single coverage constraint and the fragmentation problem, that affect its accuracy. PCL is a new rule-based classifier that overcomes these two weaknesses of decision trees by using many significant rules. We present a thorough comparison to show that our PCL method is much more accurate than C4.5, and it is also superior to Bagging and Boosting in general.