An Algorithm for Computing Attribute Reducts Based on Graph Search Strategy

Attribute reducts can discover previously unknown, non-trivial and useful abstractions from the data in large databases. However, many methods for finding attribute reducts from large data sets always meet a difficult problem of combination explosion. To overcome the problem and find some attribute reducts with high efficiency, the algorithm CARHS was proposed. The basic idea of CARHS is: 1) transform the problem into an equivalent one that searches paths, from which attribute reducts can be easily derived, from a graph; 2) employ high efficient heuristic rules during the course of depth-first search on the graph. By means of the heuristic rules, those paths that would not derive attribute reducts could be blocked as early as possible, furthermore, for those paths that would derive the same attribute reduct, only one of them could complete the course of search, and the others could be blocked as early as possible. Thus some attribute reducts could be found by CARHS with high efficiency even when dealing with huge data sets. The transformation of the problem, novel concepts, the heuristic search rules, and the algorithm CARHS were illustrated in detail by some examples. At last, The experiment on three classic UCI data sets showed the effect of the heuristic search rules and the efficiency of the algorithm CARHS

Performing Feature Selection with ACO

Summary. The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In real world problems FS is a must due to the abundance of noisy, irrelevant or misleading features. However, current methods are inadequate at finding optimal reductions. This chapter presents a feature selection mechanism based on Ant Colony Optimization in an attempt to combat this. The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to two very different challenging tasks, namely web classification and complex systems monitoring.

Rough ACO: A Hybridized Model for Feature Selection in Gene Expression Data

Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition, classification applications and in compression schemes. Rough Set Theory is one of the popular methods used, and can be shown to be optimal using different optimality criteria. This paper proposes a novel method for dimensionality reduction of a feature set by choosing a subset of the original features that contains most of the essential information, using the same criteria as the ACO hybridized with Rough Set Theory. We call this method Rough ACO. The proposed method is successfully applied for choosing the best feature combinations and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data