Learn $^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes

We have previously introduced an incremental learning algorithm Learn⁺⁺, which learns novel information from consecutive data sets by generating an ensemble of classifiers with each data set, and combining them by weighted majority voting. However, Learn⁺⁺ suffers from an inherent ldquooutvotingrdquo problem when asked to learn a new class omega_new introduced by a subsequent data set, as earlier classifiers not trained on this class are guaranteed to misclassify omega_new instances. The collective votes of earlier classifiers, for an inevitably incorrect decision, then outweigh the votes of the new classifiers´ correct decision on omega_new instances-until there are enough new classifiers to counteract the unfair outvoting. This forces Learn⁺⁺ to generate an unnecessarily large number of classifiers. This paper describes Learn⁺⁺ .NC, specifically designed for efficient incremental learning of multiple new classes using significantly fewer classifiers. To do so, Learn ⁺⁺.NC introduces dynamically weighted consult and vote (DW-CAV) , a novel voting mechanism for combining classifiers: individual classifiers consult with each other to determine which ones are most qualified to classify a given instance, and decide how much weight, if any, each classifier´s decision should carry. Experiments on real-world problems indicate that the new algorithm performs remarkably well with substantially fewer classifiers, not only as compared to its predecessor Learn⁺⁺, but also as compared to several other algorithms recently proposed for similar problems.