| Title: | Learn ++.NC: combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes |
| Author(s): | Muhlbaier MD; Topalis A; Polikar R; |
| Address: | "Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA" |
| ISSN/ISBN: | 1941-0093 (Electronic) 1045-9227 (Linking) |
| Abstract: | "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 'outvoting' 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" |
| Keywords: | "*Algorithms *Artificial Intelligence Computer Simulation Databases, Factual Image Processing, Computer-Assisted Pattern Recognition, Automated Reproducibility of Results Time Factors Volatile Organic Compounds/chemistry;" |
| Notes: | "MedlineMuhlbaier, Michael D Topalis, Apostolos Polikar, Robi eng Comparative Study Research Support, U.S. Gov't, Non-P.H.S. 2008/12/26 IEEE Trans Neural Netw. 2009 Jan; 20(1):152-68. doi: 10.1109/TNN.2008.2008326. Epub 2008 Dec 22" |
