AN INCREMENTAL GRAY RELATIONAL ANALYSIS ALGORITHM FOR MULTI-CLASS CLASSIFICATION AND OUTLIER DETECTION

摘要

The incremental classifier is superior in saving significant computational cost by incremental learning on continuously increasing training data. However, existing classification algorithms are problematic when applied for incremental learning for multi-class classification. First, some algorithms, such as neural network and SVM, are not inexpensive for incremental learning due to their complex architectures. When applied for multi-class classification, the computational cost would rise dramatically when the class number increases. Second, existing incremental classification algorithms are usually based on a heuristic scheme and sensitive to the training data input order. In addition, in case the test instance is an outlier and belongs to none of the existing classes, few classification algorithms is able to detect it. Finally, the feature selection and weighing schemes being utilized are generally risky for a "siren pitfall" for multi-class classification tasks. To address the above problems, we bring forward an incremental gray relational analysis algorithm (IGRA). Experimental results showed that, when applied for incremental multi-class classification, IGRA is stable in output, robust to training data input order, superior in computational efficiency, and also capable of detecting outliers and alleviating the "siren pitfall".