Colon Tumor Microarray Classification Using Neural Network with Feature Selection and Rule-Based Classification

The efficient feature subset selection for predictive and accurate classification is highly desirable in bioinformatic datasets. This paper proposes a method to apply our previously proposed neural network to microarray classification problem. The adjustable linguistic features are embedded in the network structure. After the training process, the informative features are selected. The network performs classification task either by the direct calculation or by rule-based approach. The structure of the three-layer feedforward neural network is designed with the consideration of useful information during the training process. The hidden layer is embedded with the linguistic feature tuning and mechanism for rule extraction. The colon tumor microarray dataset is used in the experiments. Good results from both direct calculation and from logical rules are achieved using the 10-fold cross validation. The results demonstrate the importance of the linguistic features selected by the network. The results show that the proposed method achieves better classification performance than the other previously proposed methods.