Dimensionality Reduction: An Empirical Study on the Usability of IFE-CF (Independent Feature Elimination by C-Correlation and F-Correlation) Measures
The recent increase in dimensionality of data has thrown a great
challenge to the existing dimensionality reduction methods in
terms of their effectiveness. Dimensionality reduction has
emerged as one of the significant preprocessing steps in machine
learning applications and has been effective in removing
inappropriate data, increasing learning accuracy, and improving
comprehensibility. Feature redundancy exercises great influence
on the performance of classification process. Towards the better
classification performance, this paper addresses the usefulness of
truncating the highly correlated and redundant attributes. Here,
an effort has been made to verify the utility of dimensionality
reduction by applying LVQ (Learning Vector Quantization)
method on two Benchmark datasets of 'Pima Indian Diabetic
patients' and 'Lung cancer patients'.
Keywords: Dimensionality Reduction, Feature Selection
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