Bioinspired algorithm for selecting semantically significant features in linguistic information processing tasks.

The article proposes bioheuristics for selecting informative features of classification in the problems of processing linguistic information. Bioheuristics is based on combining, generalizing, and adapting simpler heuristics depending on the current state of the solution. The experiments used a metric algorithm to automatically classify objects with k nearest neighbors and a classification tree algorithm. The effectiveness of bioheuristics was evaluated on information from the UCI repository of real and model machine learning problems. In the comparative evaluation of methods, non-parametric statistical tests were used, such as the Wilcoxon signed-rank test and Friedman test. [ABSTRACT FROM AUTHOR]

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