Differential prioritization in feature selection and classifier aggregation for multiclass microarray datasets

The high dimensionality of microarray datasets endows the task of multiclass tissue classification with various difficulties—the main challenge being the selection of features deemed relevant and non-redundant to form the predictor set for classifier training. The necessity of varying the emphases on relevance and redundancy, through the use of the degree of differential prioritization (DDP) during the search for the predictor set is also of no small importance. Furthermore, there are several types of decomposition technique for the feature selection (FS) problem—all-classes-at-once, one-vs.-all (OVA) or pairwise (PW). Also, in multiclass problems, there is the need to consider the type of classifier aggregation used—whether non-aggregated (a single machine), or aggregated (OVA or PW). From here, first we propose a systematic approach to combining the distinct problems of FS and classification. Then, using eight well-known multiclass microarray datasets, we empirically demonstrate the effectiveness of the DDP in various combinations of FS decomposition types and classifier aggregation methods. Aided by the variable DDP, feature selection leads to classification performance which is better than that of rank-based or equal-priorities scoring methods and accuracies higher than previously reported for benchmark datasets with large number of classes. Finally, based on several criteria, we make general recommendations on the optimal choice of the combination of FS decomposition type and classifier aggregation method for multiclass microarray datasets.