A Robust Hybrid Approach for Textual Document Classification

Abstract

Text document classification is an important task for diverse natural language processing based applications. Traditional machine learning approaches mainly focused on reducing dimensionality of textual data to perform classification. This although improved the overall classification accuracy, the classifiers still faced sparsity problem due to lack of better data representation techniques. Deep learning based text document classification, on the other hand, benefitted greatly from the invention of word embeddings that have solved the sparsity problem and researchers focus mainly remained on the development of deep architectures. Deeper architectures, however, learn some redundant features that limit the performance of deep learning based solutions. In this paper, we propose a two stage text document classification methodology which combines traditional feature engineering with automatic feature engineering (using deep learning). The proposed methodology comprises a filter based feature selection (FSE) algorithm followed by a deep convolutional neural network. This methodology is evaluated on the two most commonly used public datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7.7% on 20 Newsgroups and 6.6% on BBC news datasets.

[Retracted] News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model

“…However, in the face of massive and chaotic network news information, the problem that users face is no longer how to find news resources, but how to efficiently and accurately obtain information that can meet their own needs from the massive news resources [6]. erefore, in order to meet the diverse and personalized needs of online news users in the context of the big data era, effective information organization and management of online news have become an urgent problem to be solved [7].…”

Optimizing Document Classification: Unleashing the Power of Genetic Algorithms

“…The results of their experiments revealed that the CPAMF results were better than those of the cosine measure and BM25 by a healthy margin. For research article categorization, some authors have proposed hybrid approaches [19], [27], [28], [29], [30]. In these approaches, feature extraction is performed utilizing DL techniques and classification based on ML and DL methods.…”

A Comprehensive Evaluation of Metadata-Based Features to Classify Research Paper’s Topics

“…Comparison against the performance with SVM, Rocchio algorithm, Bayes, Naïve Bayes is mentioned in the paper, however, authors have not provided the table or graph results. Some authors proposed hybrid approaches for textual document classification [21] [22] [23]. In hybrid approaches, the algorithms focused on both feature extraction using deep learning and classification using machine and deep learning.…”