Abstract
In the last few decades, we have seen a tremendous increase in the amount of data available on the web. There have been significant advances in constructing knowledge bases consisting of relations from the text data. These relations are words in the text often represented as pairs (Noun, Context), for example (Disease, Symptom), which can be classified into some predefined category to give us some useful information. Categorization of relations using tolerance-rough set based semi-supervised learning algorithm (TPL) have been successfully demonstrated in several works. However, an unexplored problem is the automatic selection of hyper parameters of the TPL algorithm. This paper proposes a genetic algorithm-based approach (TPL-GA) for optimizing the hyper-parameters that are fundamental to the TPL algorithm. The proposed approach was tested on two standard datasets drawn from different domains representing two different languages: English and Hindi text.
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Shubham Agrawal, Rashad Ahmed, Anand Kumar, M., Sheela Ramanna (2022). Categorizing Relations via Semi-supervised Learning Using a Hybrid Tolerance Rough Sets and Genetic Algorithm Approach. In: Gupta, D., Khamparia, A., Khanna, A., Castillo, O. (eds) Soft Computing for Data Analytics, Classification Model, and Control. Studies in Fuzziness and Soft Computing, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-92026-5_6
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