Database Classification by Integrating a Case-Based Reasoning and Support Vector Machine for Induction

Abstract

Database classification suffers from two common problems, i.e., the high dimensionality and nonstationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a Support Vector Machine (SVM), and Genetic Algorithms to construct a decision-making system for data classification in various database applications. The model is mainly based on the concept that the historic database can be transformed into a smaller case-base together with a group of SVM models. As a result, the model can more accurately respond to the current data under classifying from the inductions by these SVM models generated from these smaller case bases. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.

Myocardial Infarction Classification Using Polynomial Approximation and Principal Component Analysis

“…The significant information will be selected to train the classifier. Here SVM [10] is selected to validate the relationship between extracted features and MI.…”