Partitioned Feature-based Classifier Model

The Partitioned Feature-based Classifier (PFC) is proposed in this paper. PFC does not use entire feature vectors extracted from the original data at once to classify each datum, but use only groups of features related to each feature vector to classify data separately. In the training stage, the contribution rate calculated from each feature vector group is drawn throughout the accuracy of each feature vector group and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the contribution rate of each feature vector group. The proposed PFC algorithm is applied to two audio data classification problems, a speech/music data classification problem and a music genre classification problem. The results demonstrate that conventional clustering algorithms can improve their classification accuracy when the proposed PFC model is used with them.