MSc Data Analytics: Topic Classification via PSO and AFSA Model

This project addresses the challenge of topic classification in large text datasets by employing optimized feature selection techniques. It proposes a model that leverages particle swarm optimization (PSO) and artificial fish swarm optimization (AFSA) algorithms to identify the most relevant subset of features from a collection of documents. The goal is to minimize classification errors by combining these optimal feature subsets, effectively categorizing documents into specific topics. The project highlights the importance of feature selection in managing high-dimensional, noisy data, offering a solution to enhance the accuracy and efficiency of topic classification in the era of big data and natural language processing. This assignment is available on Desklib, a platform offering a wide range of study tools and solved assignments for students.

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Submission date: 16-Apr-2018 12:26PM (UTC-0400)
Submission ID: 947815796
File name: 2147005_133517833_karthik1.pdf (329.27K)
Word count: 6426
Character count: 36144

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