Microarray Classification Using Sub-space Grids

Abstract

The work presented in this paper describes how sub-space grids can be employed to extract rules for micro array classification. The paper first describes principal component analysis (PCA) algorithm for obtaining sub-space grids from the projected low dimensional space. A recursive procedure is then used to obtain rules where sub-space grids form premises of rules. The extracted set of rules is evaluated on both training and testing data sets. The sub-space grids from PCA algorithm are characterized by overlapped data from different classes and use of even more than two premises in a rule does not fully address the problem of overlapped data. As such the rules obtained do not discriminate different classes accurately. To increase the effectiveness of the set of rules, multiple discriminant analysis (MDA) algorithm instead of PCA algorithm is employed to obtain sub-space grids from the projected low dimensional space. These sub-space grids from MDA algorithm improve the classification accuracy of the system. However, the size of set of rules extracted is large and these rules are sensitive to local variations associated with the data. To address these issues, the paper explores using both the PCA and MDA algorithms simultaneously fo projected low dimensional space for obtaining sub-space grids. The resulting set of rules produce better classification accuracy results. The paper discusses a comprehensive evaluation of this rule based system. The system is tested on a dataset of 62 samples (40 colon tumor and 22 normal colon tissue). The results show that the use of sub-space grids that are obtained from a projected low dimensional space of combined PCA and MDA algorithms increase the accuracy of classification results of micro array data.