Determining the maximum length of logical rules in a classifier and visual comparison of results

MethodsX

José Luis S. Jaimes¹  Ernesto Costa²  Juan M. Corchado³  José A. Castellanos-Garzón⁴ 

[1] Corresponding author at: IBSAL/BISITE Research Group, University of Salamanca, Edificio I+D+i, 37007 Salamanca, Spain.;Department of Computer Engineering, Center of Informatics and Systems, Faculty of Science and Technology, University of Coimbra, Pólo II - Pinhal de Marrocos, 3030-290 Coimbra, Portugal;Department of Computer Engineering, Center of Informatics and Systems, Faculty of Science and Technology, University of Coimbra, Pólo II - Pinhal de Marrocos, 3030-290 Coimbra, Portugal;IBSAL/BISITE Research Group, University of Salamanca, Edificio I+D+i, 37007 Salamanca, Spain;

关键词: Machine learning;    Logical rule induction;    Data mining;    Supervised learning;    Evolutionary computation;   

DOI  :  

来源: DOAJ

【 摘 要 】

Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as the classifier search method, helping the technique of applied machine learning. In this context, the knowledge representation in form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction and is based on the idea of sequential covering. We introduce genetic programming as the search method for classification-rules. From this approach, we have given results on subjects as maximum rule length, number of rules needed in a classifier and the rule intersection problem. The experiments developed on benchmark clinical data resulted in a methodology to follow in the learning method evaluation. Moreover, the results achieved compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain. • The method is based on genetic programming techniques to find rules holding each class in a dataset. • The method is approached to solve the problem of rule intersection from different classes. • The method states the maximum length of a rule to generalize.

【 授权许可】

Unknown