Text Mining: Classification of Text Documents using Granular Hybrid Classification Technique

Abstract

Since past many years, a large amount of raw data is getting converted into digital data within the information era. Maintaining and procuring the data is busy task for all the users who are willing to access the information in line with the requirements, however, the digital knowledge that's unbroken throughout this globe is not relevant in line with the need of the users. To overcome this problem classification process plays a major role to classify the data according to the need of the customer and provide relevant information. The classification algorithm is the process of extracting the information from the large data set and classifying the data which helps the customer to get the relevant information. Multi-class classification is the process of classifying more than two outcomes. Most of the algorithms produce good results when the target classes are few but as the target classes increase the accuracy reduces. There are also cases in classification where instead of classifying a category in the target function, we classify a code. Imagine we want to classify a product code from a large corpus based on the text written by a user. In our paper, we study the repercussions of a corpus which outgrows memory after vectorizing and perform a comparative analysis of various algorithms used during the process with our algorithm. We have represented the Granular Hybrid Model algorithm to classify the ocean ship food catalogue data set based on the user need and product code at a granular level and also by taking care of memory constraints which is a major drawback of normal classification algorithms. Our algorithm has represented a good accuracy of around 75% compared to other algorithms by considering the memory constraints of a huge data set of Ocean ship food catalogue. Keywords— Machine Learning, Natural Language Processing, Tf-Idf, Sklearn Technique, Granular Hybrid Classification Algorithm