Employing Decoding of Specific Error Correcting Codes as a New Classification Criterion in Multiclass Learning Problems

Error Correcting Output Codes (ECOC) method solves multiclass learning problems by combining the outputs of several binary classifiers according to an error correcting output code matrix. Traditionally, the minimum Hamming distance is adopted as the classification criterion to "vote" among multiple hypotheses, and the focus is given to the choice of error correcting output code matrix. In this paper, we apply a decoding methodology in multiclass learning problems, in which class labels of testing samples are unknown. In other words, without comparing the predicted and actual class labels, it can be known whether testing samples are classified correctly. Based on this property, a new cascade classifier is introduced. The classifier can improve the accuracy and will not result in over fitting. The analytical results show feasibility, accuracy, and the advantages of the proposed method.