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• W2540488895 abstract "We present a new approach for linear classification optimisation based on Combinatorial Refinement (ComRef) of feature weighting for cognitive signal processing in resource-limited hardware and software like in Cyber-physical systems. Despite simple construction, the approach is able to connect advantages of dimensionality reduction methods and such like combining multiple classifiers resp. Bag-of-classifiers-approaches and leads to a good generalisation ability even by use of small feature sets. Regarding generalisation ability, we benchmark the performance of ComRef on several datasets from the UCI repository. Furthermore, for an industrial dataset Motor Drive Diagnosis we show the advantage of ComRef which uses Support-Vector-Machines (SVM). In this application scenario, a trustful classifier is essential, since a small number of mis-classifications could lead to motor damages." @default.
• W2540488895 created "2016-11-04" @default.
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• W2540488895 date "2014-09-01" @default.
• W2540488895 modified "2023-09-25" @default.
• W2540488895 title "Combinatorial refinement of feature weighting for linear classification" @default.
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• W2540488895 doi "https://doi.org/10.1109/etfa.2014.7005106" @default.
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