Scaling Down Dimensions and Feature Extraction in Document Repository Classification

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Provided by: IIR Publications
Topic: Data Management
Format: PDF
In this paper, a comprehensive evaluation of two supervised feature selection methods for dimensionality reduction is performed - Latent Semantic Indexing (LSI) and Principal Component Analysis (PCA). This is gauged against unsupervised techniques like fuzzy feature clustering using hard Fuzzy C-Means (FCM). The paper is to estimate the relative efficiency of two supervised techniques against unsupervised fuzzy techniques while reducing the feature space. It is found that clustering using FCM leads to better accuracy in classifying documents in the face of evolutionary algorithms like LSI and PCA.
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