Optimization of Discrete Wavelet Transform Feature Representation and Hierarchical Classification of G-Protein Coupled Receptor Using Firefly Algorithm and Particle Swarm Optimization  
[PERSON]  
1 School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia  
2 Centre for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology,  
Universiti Kebangsamal Malaysia, Bangi 463600, Selangor, Malaysia
information between classes in a hierarchy [1]. Various methods have been introduced to overcome the weaknesses of hierarchical classification methods in past studies. However, there is still room to improve existing methods by conducting various experiments related to hierarchical classification. One of the ways to overcome the problem of hierarchical classification is to consider the feature representation of each class in the hierarchy. Each feature has a different level of relevance to represent each class in the hierarchy [2]. The use of inappropriate features may affect the performance of classification [3; 4]. There are many different feature representation techniques used to represent protein sequences. The well-known amino acid composition (AAC) feature representation method represents protein classes based on the number of amino acids in the protein sequence. However, this AAC method has the disadvantage that it does not contain protein sequence information [3]. This problem is overcome by the pseudo amino acid composition (PseAAC) feature representation method. The PseAAC feature representation method can convert protein features based on the number of amino acids and contains information on the order of amino acids in the protein sequence [5]. There are additional protein feature representation methods such as N-gram [6; 7], position-specific scoring matrix (PSSM) [8; 9], z- value [10], and a combination of several features [11]. According to [12; 13], most studies use global features with the entire protein sequence information for protein representation. Nevertheless, these global features cannot extract hidden information in protein sequences [13]. In addition, global features contain overlapping and unnecessary information [11; 14]. Discrete wavelet transform (DWT) is a feature representation method that has the ability to perform analysis at various levels [15; 16; 17]. The DWT method is suitable for representing features of biological data [13; 16; 17; 18; 19]. With the property of multiresolution analysis, DWT can provide information on protein sequence arrangement more effectively and allow biological signals to be analyzed in the frequency domain and time domain [17; 19]. This differs from signal processing methods such as Fourier transform, which can only study signals in the frequency domain [13; 15; 20; 21]. Therefore, the DWT method can provide more information compared to other feature representation methods [22; 23; 24; 25]. DWT can produce global features and local features in various decomposition levels to be analyzed, and produce features that do not have overlaps [26]. Global features are obtained from approximation DWT coefficients, while local features are obtained from detailed DWT coefficients [17]. Despite many advantages of DWT, the main problem is selecting the appropriate DWT family type and decomposition level to represent each class in the GPCR protein hierarchy. Different types of families and decomposition levels can be used in DWT. The appropriate DWT family type and decomposition level selection are important in data analysis because an accurate representation will preserve the data's important characteristics [27; 28; 29]. Furthermore, it assists in understanding the organization and complexity of this data [16; 30]. The selected DWT family needs to meet the characteristics of orthogonality, symmetry, and shape similarity with the studied data [31; 32].