Active class selection

Abstract

This paper presents Active Class Selection (ACS), a new class of problems for multi-class supervised learning. If one can control the classes from which training data is generated, utilizing feedback during learning to guide the generation of new training data will yield better performance than learning from any a priori fixed class distribution. ACS is the process of iteratively selecting class proportions for data generation. In this paper we present several methods for ACS. In an empirical evaluation, we show that for a fixed number of training instances, methods based on increasing class stability outperform methods that seek to maximize class accuracy or that use random sampling. Finally we present results of a deployed system for our motivating application: training an artificial nose to discriminate vapors. © Springer-Verlag Berlin Heidelberg 2007.