Apparatus, method, and computer program product for multi-class classification using adapted two-level binary classification models

Abstract

A multi-class classification system performs multi-class classification using an adapted, multi-level binary classification model in conjunction with pre-classification and post-classification processing. Original, multi-class training data is transformed into coarse-level adapted training data and fine-level adapted training data. In the coarse-level adapted training data, objects from the original training set are classified into either a majority classification set (representing the largest original classification set) or a non-majority classification set representing a combination of the remaining classification sets from the original training data. In the fine-level adapted training data, objects from the non-majority classification set retain their original classifications from the original training data. The adapted training data is used to train coarse-level and fine-level instances of the binary classification model, which are used to generate coarse-level and fine-level binary classifications of objects with respect to an input data set. The binary classifications are transformed into a multi-class classification.