Automated synthesis of feature functions for pattern detection

Abstract

In pattern detection systems, the general techniques of feature extraction and selection perform linear transformations from primitive feature vectors to new vectors of lower dimensionality. At times, new extracted features might be linear combinations of some primitive features that are not able to provide better classification accuracy. To solve this problem, we propose the integration of genetic programming and the expectation maximization algorithm (GP-EM) to automatically synthesize feature functions based on primitive input features for breast cancer detection. With the Gaussian mixture model, the proposed algorithm is able to perform nonlinear transformations of primitive feature vectors and data modeling simultaneously. Compared to the performance of other algorithms, such us the support vector machine, multi-layer perceptrons, inductive machine learning and logistic regression, which all used the entire primitive feature set, the proposed algorithm achieves a higher recognition rate by using one single synthesized feature function.