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- W2896097410 abstract "Neural Networks (NNs) are attractive for any classification problem. They are using extensively in the medical domain. As the medical data by nature is noisy and high-dimensional these NNs require more training time and provides poor generalization. These problems are avoided if pre-processing of data is done prior to training. Branch & Bound (B& B) based feature selection is one of the pre-processing technique extensively used to remove noisy and redundant features in the dataset. In this paper, we proposed a new criterion function based on likelihood ratio of class distributions to select more relevant features along with equal-depth binning of features. Pima Indians Diabetes dataset is chosen for experiments. Experimental results proved that proposed B&B equal-depth Binning (Bin-BB) helped in reducing the search space by reducing the height of B&B feature tree. The new criterion function helped for effective and efficient feature selection. As a result, performance of majority NN-based classifiers is improved on PID dataset." @default.
- W2896097410 created "2018-10-26" @default.
- W2896097410 creator A5053043725 @default.
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- W2896097410 date "2017-12-01" @default.
- W2896097410 modified "2023-09-22" @default.
- W2896097410 title "Bin-BB: Binning with Branch & Bound feature selection for improved diabetes classification" @default.
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- W2896097410 doi "https://doi.org/10.1109/indicon.2017.8487868" @default.
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