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Feature selection is an effective data preprocessing step to reduce the dimension of feature space and save storage space. Binary particle swarm optimization (BPSO) has been applied successfully to solve feature selection problem. But it was easy to fall into local optimal point. M2BPSO was an improved BPSO algorithm. The particles of M2BPSO were updated by using various evolutionary strategies according...
The correct prediction of protein translation initiation from messenger RNA (mRNA) is an important activity for genomic annotation. This problem is known to be highly imbalanced, since each molecule has a single mRNA translation initiation site and several others which are not initiator AUGs. In this context, the present work has focused on undersampling methods for balancing class distribution proposed...
The Support Vector Machine method has a good learning and generalization ability. Unfortunately, there are no comprehensive theories to guide the parameter selection of the SVM, which largely limits its application. In order to get the optimal parameters automatically, researchers have tried a variety of methods. Using genetic algorithms to optimize parameters of an SVM Classifier has become one of...
A method is proposed based on application of Error Correcting Output Codes Support Vector Machine (ECOC-SVM) in order to get better results of speech recognition. Some uncorrelated SVMs are constructed based on ECOC matrix codes to improve the integrated performance of fault tolerance of classification model. This paper gives four commonly-used encodings of ECOC. By comparing the results with that...
A kind of method of SVM for multi-class problems was given in this paper. This method is based on PCA Support Vector Machine coding method. After experimenting, it is better than one-against-one Method and one-against-the -rest Method. This SVM for multi-class method saves time and enhances precision of forecast. In based on principal component analysis SVM method, coding multi-class method classes'...
This study is focused on the single-trial classification of auditory event-related potentials elicited by sound stimuli from different spatial directions. Five naϊve subjects were asked to localize a sound stimulus reproduced over one of 8 loudspeakers placed in a circular array, equally spaced by 45°. The subject was seating in the center of the circular array. Due to the complexity of an eight...
Automatic prediction of protein three-dimensional structures from its amino acid sequence has become one of the most important researched fields in bioinformatics. With that increases the importance of determining the quality of these protein models. Protein three-dimensional structure evaluation is a complex problem in computational structure biology. We attempt to solve this problem using SVM and...
Chemokine receptors represent a prime target for the development of novel therapeutic strategies in a variety of disease processes. The prediction of interesting proteins types by computational methods can provide new clues in functional studies of uncharacterized proteins without performing extensive experiments. Support vector machine (SVM) is a new kind of approach to supervised pattern classification...
In this study, we attempted to identify the most influential features of input data for neural decoding across different decoders. For the example of decoders, we used support vector machine (SVM), k-nearest neighbor method (KNN) and canonical discriminate analysis (CDA) and decoded the tone-induced neural activities in a rat auditory cortex into the test tone frequencies. We proposed an algorithm...
The support vector machine (SVM) method based on n-peptide composition (Yu et al, Proteins: Struct. Funct. Genet. 2003:50:531-536) is used to predict the subcellular localizations of proteins. For an unbiased assessment of the results, we apply our approach to two independent data sets: one set consisting of two parts (Reinhardt and Hubbard, Nucleic Acids Res. 1998; 26:2230-2236): the prokaryotic...
Cancer classification using gene expression data has the great importance in bioinformatics and is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis and drug discovery. Error correcting output coding (ECOC) is a method to design multiple classifier systems (MCS), which reduces a multi-class problem into some binary sub-problem. A key issue in design of...
This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used...
Secondary structure prediction of proteins has increasingly been a central research area in bioinformatics. In order to know which data encoding approach is more effective whiling predicting secondary structure using SVM, five approaches: ENCOrth, ENCFive, ENCCodBas, ENCCodExt and ENCProf are discussed in this paper. The results of data encoding are used as input of SVM. By performing ENCProf approach,...
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