Towards a Feature Selection for Multi-label Text Classification in Big Data

Amazal, Houda; Ramdani, Mohammed; Kissi, Mohamed

doi:10.1007/978-3-030-45183-7_14

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In most of the works on FS, researchers have worked on binary classification rather than textual datasets. Selecting the most relevant features from a large volume of data has become the most significant challenge in many applications, especially in text classification [30]. As the amount of the data continues to grow, conventional algorithms cannot adapt in terms of memory requirements, execution time, and efficiency of the results.…”

Section: Related Workmentioning

confidence: 99%

A New Big Data Feature Selection Approach for Text Classification

Amazal

Kissi

2021

Scientific Programming

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Feature selection (FS) is a fundamental task for text classification problems. Text feature selection aims to represent documents using the most relevant features. This process can reduce the size of datasets and improve the performance of the machine learning algorithms. Many researchers have focused on elaborating efficient FS techniques. However, most of the proposed approaches are evaluated for small datasets and validated using single machines. As textual data dimensionality becomes higher, traditional FS methods must be improved and parallelized to handle textual big data. This paper proposes a distributed approach for feature selection based on mutual information (MI) method, which is widely applied in pattern recognition and machine learning. A drawback of MI is that it ignores the frequency of the terms during the selection of features. The proposal introduces a distributed FS method, namely, Maximum Term Frequency-Mutual Information (MTF-MI), based on term frequency and mutual information techniques to improve the quality of the selected features. The proposed approach is implemented on Hadoop using the MapReduce programming model. The effectiveness of MTF-MI is demonstrated through several text classification experiments using the multinomial Naïve Bayes classifier on three datasets. Through a series of tests, the results reveal that the proposed MTF-MI method improves the classification results compared with four state-of-the-art methods in terms of macro-F1 and micro-F1 measures.

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Section: Related Workmentioning

confidence: 99%

A New Big Data Feature Selection Approach for Text Classification

Amazal

Kissi

2021

Scientific Programming

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“…In the work of Amazal et al [1], the authors address the multi-label feature selection task proposing a weighted Chi-square feature selection approach called Distributed Category Term Frequency Based on Chi-square (CTF-CHI), and used a Multinominal Naive Bayes (MNB) classifier to evaluate the efficiency of a selected subset of features. The authors performed feature selection by transforming the original problem into a single-label one.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection for Hierarchical Multi-label Classification

Silva

Cerri

2021

Advances in Intelligent Data Analysis XIX

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In this work we study how conventional feature selection methods can be applied to Hierarchical Multi-label Classification Problems. In Hierarchical Multi-label Classification, instances can belong to two or more classes (labels) simultaneously, where such classes are hierarchically structured. Feature selection plays an important role in Machine Learning classification tasks, once it can effectively reduce the dataset dimensionality by removing irrelevant and/or redundant features, improving classification accuracy. Although many relevant realworld problems are from the hierarchical and multi-label domains, the majority of the related researches address the feature selection task focusing on single-label problems. In many works, even when the proposal deals with multi-label problems, the classes are not associated with a hierarchical structure. Therefore, in this work we study how feature selection can be applied in the Hierarchical Multi-label Classification context. For this, we propose four hierarchical strategies combining the Binary Relevance (BR) and Label Powerset (LP) multi-label transformations with the attribute evaluators ReliefF (RF) and Information Gain (IG). We tested our strategies on 10 real-world datasets from the functional genomic field, commonly used in Hierarchical Multi-label Classification works. As main results, three of the four proposed strategies produced some relevant subsets of features, while keeping predictive performances in comparison to the use of the complete set of features.

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Enhancing Multi-Class Text Classification With Apriori-Based Feature Selection

Er,

Bilgin

2024

Middle East Journal of Science

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: In the field of Natural Language Processing, selecting the right features is crucial for reducing unnecessary model complexity, speeding up training, and improving the ability to generalize. However, the multi-class text classification problem makes it challenging for models to generalize well, which complicates feature selection. This paper investigates how feature selection impacts model performance for multi-class text classification, using a dataset of projects completed by TÜBİTAK TEYDEB between 2009 and 2022. The study employs LSTM, a deep learning method, to classify the projects into nine different industries based on various attributes. The paper proposes a new feature selection approach based on the Apriori algorithm, which reduces the number of attribute combinations considered and makes model training more efficient. Model performance is evaluated using metrics like accuracy, loss, validation scores, and test scores. The key findings are that feature selection significantly affects model performance, and different feature sets have varying impacts on performance.

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Towards a Feature Selection for Multi-label Text Classification in Big Data

Cited by 3 publications

References 27 publications

A New Big Data Feature Selection Approach for Text Classification

A New Big Data Feature Selection Approach for Text Classification

Feature Selection for Hierarchical Multi-label Classification

Enhancing Multi-Class Text Classification With Apriori-Based Feature Selection

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