Wavelet neural network initialization using LTS for DNA sequence classification

Abstract

In this paper, we present a new approach for DNA sequence classification. The proposed approach is based on using the Wavelet Neural Network (WNN) and the k-means algorithm. The satisfying performance of the Wavelet Neural Networks (WNN) depends on an appropriate determination of the WNN structure. Our approach uses the Least Trimmed Square (LTS) and the Gradient Algorithm (GA) to solve the architecture of the WNN. The initialization of the Wavelet Neural Network is solved by using the Least Trimmed Square (LTS) method, which is applied for selecting the wavelet candidates from the Multi Library of the Wavelet Neural Networks (MLWNN) for constructing the WNN. Besides, the Gradient Algorithm (GA) is implemented for training the WNN in our method. The GA is used to solve the structure and learning of the WNN. This algorithm is applied to adjust the parameters of WNN. The performance of the WNN is investigated by detecting the simulating and real signals in white noise. The proposed method has been able to optimize the wavelet neural network and classify the DNA sequences. In this study, the LTS model is compared to the two initialization algorithms: Residual Based Regressor Selection (RBRS) and Stepwise Regressor Selection by Orthogonalization (SRSO). The LTS algorithm is to find the regressors, which provide the most significant contribution to the approximation of error reduction. The advantage of the LTS algorithm is to select the candidate wavelet from the MLWNN. This wavelet can reduce the approximation error. Our aim is to construct classifier method that gives highly accurate results. This classifier permits to classify the DNA sequence of organisms. The classification results are compared to other classifiers. The experimental results have shown that the WNN-LTS model outperformed the other classifier in terms of both the running time and clustering. In this paper, our system consists of three phases. The first one, which is called transformation, is composed of three sub steps; binary codification of DNA sequences, Fourier Transform and Power Spectrum Signal Processing. The second section is the approximation; it is empowered by the use of Multi Library Wavelet Neural Networks (MLWNN). Finally, the third section, which is called the classification of the DNA sequences.