Rough Sets and Colonies of Artificial Ants for the Improvement of Training Sets
Carmen F. Rey-Benguría
Carmen f. Rey Benguría*, Educational Center “José Martí”, University of Ciego de Ávila, Ciego de Ávila, Cuba.
Manuscript received on March 29, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 995-1000 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7325049420/2020©BEIESP | DOI: 10.35940/ijeat.D7325.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Improving training sets is an area of active research within l to Artificial Intelligence. In particular, it is of particular interest in supervised classification systems, where the quality of training data is crucial. This paper presents a new method for the improvement of training sets, based on approximate sets and artificial ant colonies. The experimental study carried out with international databases allows us to guarantee the quality of the new algorithm, which has a high efficiency.
Keywords: Classification, artificial intelligence, data preprocessing, algorithms.