Wavelet Neural Networks for DNA Sequence Classification Using the Genetic Algorithms and the Least Trimmed Square

Abstract

This paper presents a structure of the Wavelet Neural Networks used to classify the DNA sequences. The satisfying performance of the Wavelet Neural Networks (WNN) depends on an appropriate determination of the WNN structure optimization problem. In this paper we present a new method to solve this problem based on Genetic Algorithm (GA) and the Least Trimmed Square (LTS). The GA is used to solve the structure and the learning of the WNN and the LTS algorithm is applied to select the important wavelets. First, the scale of the WNN is managed by using the time-frequency locality of wavelet. Furthermore, this optimization problem can be solved efficiently by Genetic Algorithm as well as the LTS method to improve the robustness. The performance of the Wavelet Networks is investigated by detecting the simulating and the real signals in white noise. The main advantage of this method can guarantee the optimal structure of the WNN. The experimental results have indicated that the proposed method (WNN-GA) with the k-means algorithm is more precise than other methods. The proposed method has been able to optimize the wavelet neural network and classify the DNA sequences. Our goal is to construct a predictive approach that is highly accurate results. In fact, our approach allows avoiding the complex problem of form and structure in different groups of organisms. The experimental results are showed that the WNN-GA model outperformed the other models in terms of both the clustering results and the running time. In this study, we present our system which consists of three phases. The first one is the transformation, is composed of two sub steps; the binary codification of the DNA sequences and the Power Spectrum Signal Processing. The second step is the approximation; it is empowered by the use of the Multi Library Wavelet Neural Networks (MLWNN). Finally, the third one is the clustering of the DNA sequences, is realized by applying the algorithm of the k-means algorithm.