Abstract
Multi-label classification refers to the problem of associating an object with multiple labels. This problem has been successfully addressed from the perspective of problem transformation and adaptation of algorithms. Multi-Label k-Nearest Neighbour (MLkNN) is a lazy learner that has reported excellent results, still there is room for improvements. In this paper we propose a modification to the MLkNN algorithm for the solution to problems of multi-label classification based on the Extended Rough Set Theory. More explicitly, the key modifications are focused in obtaining the relevance of the attributes when computing the distance between two instances, which are obtained using a heuristic search method and a target function based on the quality of the similarity. Experimental results using synthetic datasets have shown promising prediction rates. It is worth mentioning the ability of our proposal to deal with inconsistent scenarios, a main shortcoming present in most state-of-the-art multi-label classification algorithms.
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Pérez, G., Bello, M., Nápoles, G., García, M.M., Bello, R., Vanhoof, K. (2018). Expanding MLkNN Using Extended Rough Set Theory. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_28
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