PSO-based feature extraction for high dimension small sample

With the development of application areas of machine learning, we are confronted with more and more small sample datasets. The key to these applications is to solve the problem of mining useful information from these data. There are supervised and non-supervised feature extraction methods, linear or non-linear feature extraction methods. Some methods are not suitable for specific fields, so combing different extraction methods becomes a reasonable solution. We propose an algorithm to combine different extraction methods based on decision level fusion. With the difficulty of selecting parameters in feature extraction algorithms, we use PSO algorithm to find the best parameters value. The experiments on UCI datasets show the validity of our algorithms.