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.
Early diagnosis of Alzheimer's disease (AD) is becoming an increasingly important healthcare concern. Prior approaches analyzing event-related potentials (ERPs) had varying degrees of success, primarily due to smaller study cohorts, and the inherent difficulty of the problem. A new effort using multiresolution analysis of ERPs is described. Distinctions of this study include analyzing a larger cohort, comparing different wavelets and different frequency bands, using ensemble-based decisions and, most importantly, aiming the earliest possible diagnosis of the disease. Surprising yet promising outcomes indicate that ERPs in response to novel sounds of oddball paradigm may be more reliable as a biomarker than the more commonly used responses to target sounds.
Abstract. An ensemble of classifiers based algorithm, Learn++, was recently introduced that is capable of incrementally learning new information from datasets that consecutively become available, even if the new data introduce additional classes that were not formerly seen. The algorithm does not require access to previously used datasets, yet it is capable of largely retaining the previously acquired knowledge. However, Learn++ suffers from the inherent "out-voting" problem when asked to learn new classes, which causes it to generate an unnecessarily large number of classifiers. This paper proposes a modified version of this algorithm, called Learn++.MT that not only reduces the number of classifiers generated, but also provides performance improvements. The out-voting problem, the new algorithm and its promising results on two benchmark datasets as well as on one real world application are presented.
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