Optimal Feature Selection for Activity Recognition based on Ant Colony Algorithm

Feature selection is an important task which can significantly affect the performance of a pattern recognition system. In this paper, we present a feature selection algorithm based on Ant Colony Optimization (ACO) for human activity recognition. The algorithm incorporates the classification performance into the state transition rule, and uses the pheromone of the ant colony to find the optimal feature set, then selects feature set with small size and high classification accuracy. We perform experiments on human activity dataset using four classifiers, Naive Bayes, Random Forest, K-Nearest Neighbor and Support Vector Machine(NB, RF, KNN and SVM). The results show that the proposed method can improve the recognition accuracy, and it is comparable to other feature selection methods.