A Class-Incremental Classification Method Based on Support Vector Machine
Praneet Prabhakar Sherki, Vanraj Vala · 2020
Incremental classification has become a hot research topic due to ever increasing availability of data, finding its application in fields like image and sound analysis. As more data is collected, we may get instances which do not belong to any of the classes previously seen by the classification model. To incorporate the new class, typical multi-class classification methods require retention of complete previous data, making the training process memory intensive. This paper puts forth a novel approach to class-incremental classification using support vector machines (SVM) and a subset of training data known as candidate support vectors (CSV). By using these CSVs, the proposed method facilitates addition of a new class to multiclass SVM classifier trained in one-vs-all fashion. Experiments on different datasets achieve accuracy comparable to complete batch training, while retaining only a small subset of the training data.