Algorithmic cache of sorted tables for feature selection

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Feature selection is a mechanism used in Machine Learning to reduce the complexity and improve the speed of the learning process by using a subset of features from the data set. There are several measures which are used to assign a score to a subset of features and, therefore, are able to compare them and decide which one is the best. The bottle neck of consistence measures is having the information of the different examples available to check their class by groups. To handle it, this paper proposes the concept of an algorithmic cache, which stores sorted tables to speed up the access to example information. The work carries out an empirical study using 34 real-world data sets and four representative search strategies combined with different table caching strategies and three sorting methods. The experiments calculate four different consistency and one information measures, showing that the proposed sorted tables cache reduces computation time and it is competitive with hash table structures.