|Name of the Method||Hybrid||Non-Hybrid||Key Idea|
|Signal feature extraction by Fuzzy Neural Network ||–||▪ The Approach combines the wavelet transform with fuzzy theory to improve the limitation of applying traditional fault diagnosis method to the diagnosis of multi-concurrent vibrant faults of aero-engine.|
|Rough set and weighted LS-SVM ||–||▪ A hybrid model, which combines rough set theory and least square support vector machine to forecast the agriculture irrigation water demand.|
|Artificial Neural Network in Agricology ||–||▪ The survey was based on particular problem type, type of input, techniques used and results.|
|SNR-FFNN ||–||▪ Comparison results of two approaches for selecting biomarkers from Leukemia dataset for feedforward neural network are given.
▪ The first approach implements k-means clustering and signal-to-noise ratio (SNR) for gene ranking, the top-scored genes from each cluster is selected and given to the classifiers.
▪ The second approach uses the signal to noise ratio ranking for feature selection.
|Hybrid GA approach ||–||▪ The model accommodates multiple feature selection criteria.
▪ Find a small subset of feature that performs well for a particular inductive learning algorithm of interest to build the classifier.
▪ The subset selection criteria used are entropy-based feature ranking, t-statistics, SVM-Ref, GA as induction algorithm.
|SNR-PSO ||–||▪ The proposed method is divided into two stages,
▪ The first stage uses k-means clustering and SNR score to rank each gene in every cluster.
▪ The top scored genes from each cluster are gathered and a new feature subset is generated. In the second stage, the new feature subset is used as input to the PSO and optimized feature subset is produced.
▪ Support vector machine (SVM), k-nearest neighbor (k-NN) and Probabilistic Neural Network (PNN) are used as evaluators
▪ Leave one out cross validation approach is used for validation.
|PSO-Decision theoretic Rough set ||–||▪ The author proposes a new PSO based wrapper, single objective FS approach by developing new initialization and updating mechanisms.|
|Redundant Gene selection using PSO(RGS-PSO) ||–||▪ Redundant gene selection using PSO (RGS-PSO) is a novel approach.
▪ Where the fitness function of PSO explicitly measures feature relevance and feature redundancy simultaneously.
|ACO-BPNN [54, 56]||–||▪ The ant colony optimization (ACO) algorithm is introduced to select genes relevant to cancers.
▪ The multi-layer perceptrons (MLP) and support vector machine (SVM) classifiers are used for cancer classification.
|BBO-RF,BBO-SVM ||–||▪ Two-hybrid techniques, Biogeography – based Optimization – Random Forests (BBO – RF) and BBO – SVM (Support Vector Machines) with gene ranking as a heuristic, for microarray gene expression analysis is proposed.
▪ The BBO algorithm generates a population of candidate subset of genes, as part of an ecosystem of habitats, and employs the migration and mutation processes across multiple generations of the population to improve the classification accuracy.
▪ The fitness of each gene subset is assessed by the classifiers – SVM and Random Forests
|Wrapper using KNN [60, 61], Wrapper using 1-NN ||–||▪ Using the Naïve Bayes learner, the authors perform wrapper feature selection followed by classification, using four classifiers (Naïve Bayes, Multilayer Perceptron, 5-Nearest Neighbor, and Support Vector Machines).
▪ The above results are compared to the classification performance without feature selection.
|Bat Algorithm –Rough set method [124-127]||–||▪ A fitness function based on rough-sets is designed as a target for the optimization.
▪ The used fitness function incorporates both the classification accuracy and a number of selected features and hence balances the classification performance and reduced size.
|Improved Ant Colony Optimization-SVM [53-55, 128]||–||▪ A nature inspired and novel FS algorithm based on standard Ant Colony Optimization (ACO), called improved ACO (IACO), was used to reduce the number of features by removing irrelevant and redundant data.
▪ The selected features were fed to support vector machine (SVM), a powerful mathematical tool for data classification, regression, function estimation and modeling processes, in order to classify major depressive disorder (MDD) and Bipolar disorder (BD) subjects.
|Constructive approach for Feature Selection(CAFS) ||–||▪ The vital aspect of this wrapper algorithm is the automatic determination of NN architectures during the FS process.
▪ It uses a constructive approach involving correlation information in selecting features and determining NN architectures.
|Wrapper ANFIS-ICA method ||–||▪ The paper presents a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System)-ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick.
▪ Two approaches of Raw-based and Signal-based are devised to extract the model's input variables with 15 and 24 features, respectively.
▪ In this model, the ANFIS prediction results are used as a cost function of wrapper model and ICA is used to select the most appropriate features.