In traditional classification setting, training data are represented as a single table, where each row corresponds to an example and each column to a predictor variable or the targ...
In this paper, we examine an emerging variation of the classification problem, which is known as the inverse classification problem. In this problem, we determine the features to b...
In this paper the Karnaugh and Quine-McCluskey methods are used for symbolic classification problem, and then these methods are compared with other famous available methods. Becau...
In this paper we examine different linguistic features for sentimental polarity classification, and perform a comparative study on this task between blog and review data. We found...
Achieving high classification accuracy is a major challenge in the diagnosis of cancer types based on gene expression profiles. These profiles are notoriously noisy in that a larg...