Abstract
Information Retrieval (IR) systems play a crucial role in identifying relevant documents from a collection based on user queries. Popular examples of IR systems include search engines like Google, which locate web documents that match given words. In library settings, IR systems are employed to search digital records containing information about books rather than the books themselves. The primary objective of this research is to automatically classify information within a table. Categorization is a fundamental requirement for text retrieval systems. This research encompasses three key steps: Pre-processing, Searching, and Classification. For the searching phase, two existing algorithms, Boyer Moore and Brute Force, are utilized, and Naïve Bayes based searching algorithm is proposed. In the classification phase, existing algorithms, Linear Regression, Random Forest Regression and Kernal Ridge Regression are employed and enhanced algorithm called Enhanced Kernal Ridge for Text Categorization (EKRTC) is used. Experimental results demonstrate that EKRTC achieves the highest accuracy.
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