Abstract
In the past few years, there has been rampant growth in the amount of complex documents that stand in need of a deeper understanding of machine learning methods to classify them in many applications. The success of these methods depends on their ability to understand complex patterns and nonlinear relationships in data. Yet, finding the right structures, architectures, and techniques for text classification is often a challenge for researchers. In this article, we present an automated document classification system based on two axes; the first regard the processing of natural language (NLP), along with the second that focuses on Machine Learning (ML) algorithms. In addition, a hybrid system that combines the best of classification models in a single strong system with a very high percentage of accuracy that we came to give rise to with the genetic algorithms (GA).
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Ahle Touate, C., Zougagh, H. (2021). Classification of Documents Using Machine Learning and Genetic Algorithms. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2021. Lecture Notes in Business Information Processing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-030-76508-8_5
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DOI: https://doi.org/10.1007/978-3-030-76508-8_5
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