Protein Fold Prediction Problem Using Ensemble of Classifiers

A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition

“…In the experiments, the -fold cross-validation 1 procedure is used to find the classification performance for different feature extraction techniques. The values of k are taken to be 5,6,7,8,9, and 10. For the SVM classifier, RBF kernel is used.…”

“…It is also shown that by fusion of features the recognition rates can be improved [30]- [33]. For the latter task case, several classifiers have been developed or used including linear discriminant analysis [34], [35], Bayesian classifiers [2], Bayesian decision rule [36], k-nearest neighbor [25], [37], Hidden Markov model [38], [39], artificial neural network [40], [41], support vector machine (SVM) [6], [20], [21], [42], [43], and ensemble classifiers [20], [33], [41], [44], [45]. Among these classifiers, SVM (or SVM-based for ensemble strategy) classifier exhibits quite promising results [21], [26], [27].…”

mGOF-loc: A novel ensemble learning method for human protein subcellular localization prediction

“…A well-defined ensemble classifier is recognized to address computational, statistical, and representational issues better than single classifiers [35,58,59]. The diversity of the basic classifiers in the ensemble process is proved to be important to enhance the performance of an ensemble classifier [60,61].…”

Protein Fold Recognition Using Genetic Algorithm Optimized Voting Scheme and Profile Bigram

“…Ferredoxin-like 13 27 Small inhibitors, toxins, lectins 13 27 Furthermore, some of the classification techniques that have been explored include Linear Discriminant Analysis [24], K-Nearest Neighbors [25], Bayesian Classifiers [26]- [28], Support Vector Machines (SVM) [19]- [21], [28]- [30], Artificial Neural Networks (ANN) [31]- [33] and ensemble classifiers [34], [35]. Out of these mentioned classification techniques, SVM has showed promising results in protein fold recognition problem.…”