Hybrid Model Based Feature Selection Approach Using Kernel PCA for Large Datasets

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Due Knowledge discovery from large data sets by means of traditional data mining approaches has been established to be tedious due to large size in both dimension and samples. In real applications, data sets frequently consist of many noisy, unnecessary and unrelated features, resulting in humiliating the classification accuracy and increasing the difficulty exponentially. Owing to the intrinsic nature, the analysis of the quality of data sets is tricky and very partial approaches about this problem can be present in the survey. There is a requirement for quality of data; therefore the quality of data is important at the end. Data preprocessing, feature selection is a commonly used data mining techniques which selects a subset of informative attributes or variables to construct models relating data then classification is made. Through by eliminating redundant and unrelated or noise features, dimensionality reduction of the data for feature selection can increase the predictive accuracy and the clarity of the predictors or classifiers. Various feature selection algorithms with different selection condition and the classification algorithms have been presented by the researchers. In this paper, an Enhanced KNN method is used to find the missing values from the whole dataset by preprocessing process. Then the feature selection of the datasets is done using Enhanced Genetic Algorithm combined with Kernel PCA_SVM Algorithm. Experimental results shows that the proposed approaches perform better when compared to other existing techniques.
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Datasets; Enhanced KNN; Dimensionality Reduction; Feature Selection; Preprocessing

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