Sequence Based Feature Selection using ant colony optimization

Feature selection is a process with the aim of diminishing irrelevant and redundant features from original set. Some of features which are called irrelevant not only provide no useful information to classifier but also put it in pitfalls. Some other are neutral means no conducting and no misleading. This group is called redundant features. In this paper, we introduced a new algorithm based on ant colony optimization (ACO) with the aid of statistical information from training dataset to cut down mentioned groups of features. The proposed algorithm is called Sequence Based Feature Selection (SFS) able to get target subset cardinality as an input or not. In this paper ants are applying two factors related to statistical information as heuristic desirability to remove irrelevant and redundant groups of features. These information are well-known Fisher-Score and Correlation respectively. Our algorithm utilizes a fully connected graph of nodes to model the problem and put the pheromone on nodes rather than edges. In addition it uses a dynamic desirability rather than a pre-calculated static one. In each iteration every ant comes up with a sequence of features which is passed to the next step for choosing a subsequence of it starting from beginning. The proposed algorithm has been tested against some well-known datasets and its performance compared with recently represented algorithms. The results indicates that using dynamic two factors desirability in fully connected graph is more effective in removing redundant features than Binary approaches.