| Classification is an important data mining task, widely used in numerous real world applications. It aims at exploring through data objects (training set) to find a set of rules which determine the class of each object according to its attributes. These rules are later used to build a classifier to predict the class or missing attribute value of unseen objects whose class might not be known. Classification approaches are mainly based on either machine learning techniques or association rule-based algorithms, known as associative classification. Despite their good performance in real-world applications, both approaches have some shortcomings. Machine learning algorithms based classification suffers from understandability and interpretability problems, while associative classifiers have efficiency issues.; In this study, we propose a novel classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the knowledge represented by class association rules and the power of the SVM algorithm, to construct an efficient and accurate classifier model that overcomes the drawbacks of machine learning and associative classification algorithms. Instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning module of the SVM algorithm. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to microarray gene expression datasets. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results demonstrate the applicability, efficiency and effectiveness of the proposed model. |
