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The recent increase in dimensionality of data has thrown a great challenge tothe existing dimensionality reduction methods in terms of their effectiveness.Dimensionality reduction has emerged as one of the significant preprocessingsteps in machine learning applications and has been effective in removinginappropriate data, increasing learning accuracy, and improvingcomprehensibility. Feature redundancy exercises great influence on theperformance of classification process. Towards the better classificationperformance, this paper addresses the usefulness of truncating the highlycorrelated and redundant attributes. Here, an effort has been made to verifythe utility of dimensionality reduction by applying LVQ (Learning VectorQuantization) method on two Benchmark datasets of 'Pima Indian Diabeticpatients' and 'Lung cancer patients'.

Dimensionality Reduction: An Empirical Study on the Usability of IFE-CF  (Independent Feature Elimination- by C-Correlation and F-Correlation)  Measures