Interpretation of Support Vector Machines by means of Fuzzy Rule-Based Systems
نویسندگان
چکیده
Support Vector Machines (SVM) have demonstrated their ability in solving classification problems in an optimal way with a solid mathematical background. In this paper we improve the interpretability of SVM’s by showing that every SVM is exactly represented by a Fuzzy Rule BasedSystem, for every kernel function used. Nevertheless, this system is in some way compact in their rules and for that reason, we introduce another FRBS, called δ-FRBS, that approximates it and which is suitable to decompose its rules in simple fuzzy propositions. We show it with an example in the last section.
منابع مشابه
A QUADRATIC MARGIN-BASED MODEL FOR WEIGHTING FUZZY CLASSIFICATION RULES INSPIRED BY SUPPORT VECTOR MACHINES
Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only considers both accuracy and generalization criteria in a single objective fu...
متن کاملMining Biological Repetitive Sequences Using Support Vector Machines and Fuzzy SVM
Structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. Biggest class of the repetitive subsequences is “Transposable Elements” which has its own sub-classes upon contexts’ structures. Many researches have been performed to criticality determine the structure and function of repetitiv...
متن کاملFuzzy Apriori Rule Extraction Using Multi-Objective Particle Swarm Optimization: The Case of Credit Scoring
There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...
متن کاملFuzzy Apriori Rule Extraction Using Multi-Objective Particle Swarm Optimization: The Case of Credit Scoring
There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...
متن کاملRobustified distance based fuzzy membership function for support vector machine classification
Fuzzification of support vector machine has been utilized to deal with outlier and noise problem. This importance is achieved, by the means of fuzzy membership function, which is generally built based on the distance of the points to the class centroid. The focus of this research is twofold. Firstly, by taking the advantage of robust statistics in the fuzzy SVM, more emphasis on reducing the im...
متن کامل