Se pretende optimizar el clasificador K-NN [1] resolviendo el problema al que llamaremos Aprendizaje de pesos en características (APC)[2].
Los ficheros de src son los siguientes \begin{itemize} \item \textbf{Instancias APC}: Ficheros con datos. \item \textbf{algoritmos búsqueda}: Generar vecinos. \item \textbf{learner}: Abstrae algoritmos de búsqueda local. \item \textbf{resultados}: Carpeta con csv de los resultados y los ficheros que los generan. \item \textbf{utils}: Funciones auxiliares útiles. \end{itemize}
Para ejecutarlo basta con ejecutar \texttt{make result}.
[1] Título: $K$ vecinos más próximos. Fuente: Wikipedia. URL: https://es.wikipedia.org/wiki/K_vecinos_más_próximos Fecha de consulta: 14-03-22
[2] Muhammad Atif Tahir, Ahmed Bouridane, Fatih Kurugollu, Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier, Pattern Recognition Letters, Volume 28, Issue 4, 2007, Pages 438-446, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2006.08.016. (https://www.sciencedirect.com/science/article/pii/S0167865506002303) Abstract: Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine. Keywords: Tabu Search; K-NN classifier; Feature selection; Feature weighting; Prostate cancer diagnosis