ENHANCING MULTI-CLASS TEXT CLASSIFICATION WITH APRIORI-BASED FEATURE SELECTION

Year 2024, Volume: 10 Issue: 1, 41 - 57, 30.06.2024

Maide Feyza Er Turgay Tugay Bilgin

https://doi.org/10.51477/mejs.1475196

Abstract

: 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.

Keywords

Feature Selection, LSTM, NLP, Text Classification

Ethical Statement

Our study does not cause any harm to the environment and does not involve the use of animal or human subjects. Therefore, it was not necessary to obtain an Ethics Committee Report.

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