The topic of feature selection has become one of the hottest subjects in machine learning over the last few years. The results of evolutionary algorithm selection have also been promising, along with standard feature selection algorithms. For K-Nearest Neighbor (KNN) classification, this paper presents a hybrid filter-WRAPPER algorithm based on Equilibrium Optimization (EO). With respect to the selected feature subset, the filter model is based on a composite measure of feature relevance and redundancy. The WRAPPER model consists of a binary Equilibrium Optimization (BEO). The hybrid algorithm is called filter-based BEO (FBBEO). By combining filters and WRAPPERs, FBBEO achieves a unique combination of efficiency and accuracy. In the experiment, 11 standard datasets from the UCI repository were utilized. Results indicate that the proposed method is effective in improving the classification accuracy and selecting the best optimal features subsets with the least number of features.

Feature selection for various applications has been carried out for many years in many different research areas. However, there is a trade-off between finding feature subsets with minimum length and increasing the classification accuracy. In this paper, a filter-WRAPPER feature selection approach based on fuzzy-rough gain ratio is proposed to tackle this problem. As a search strategy, a modified Ant Colony Optimization (ACO) algorithm is applied on filter phase. ACO has been approved to be a suitable solution in many difficult problems with graph search space such as feature selection. Choosing minimal data reductions among the subsets of features with first and second maximum accuracies is the main contribution of this work. To verify the efficiency of our approach, experiments are performed on 10 well-known UCI data sets. Analysis of the experimental results demonstrates that the proposed approach is able to satisfy two conflicting constraints of feature selection, increasing the classification accuracy as well as decreasing the length of the reduced subsets of features.

Data dimensions and networks have grown exponentially with the Internet and communications. The challenge of high-dimensional data is increasing for machine learning and data science. This paper presents a hybrid filter-WRAPPER feature selection method based on Equilibrium Optimization (EO) and Simulated Annealing (SA). The proposed algorithm is named Filter-WRAPPER Binary Equilibrium Optimizer Simulated Annealing (FWBEOSA). We used SA to solve the local optimal problem so that EO could be more accurate and better able to select the best subset of features. FWBEOSA utilizes a filtering phase that increases accuracy as well as reduces the number of selected features. The proposed method is evaluated on 17 standard UCI datasets using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers and compared with ten state-of-the-art algorithms (i.e., Binary Equilibrium Optimizer (BEO), Binary Gray Wolf Optimization (BGWO), Binary Swarm Slap Algorithm (BSSA), Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), Binary Social Mimic Optimization (BSMO), Binary Atom Search Optimization (BASO), Modified Flower Pollination Algorithm (MFPA), Bar Bones Particle Swarm Optimization (BBPSO) and Two-phase Mutation Gray Wolf Optimization (TMGWO)). Based on the results of the SVM classification, the highest level of accuracy was achieved in 13 out of 17 data sets (76%), and the lowest number of selected features was achieved in 15 out of 17 data sets (88%). Furthermore, the proposed algorithm using class KNN achieved the highest accuracy rate in 14 datasets (82%) and the lowest selective feature rate in 13 datasets (76%).

In this paper, we develop a non-visual automatic WRAPPER to extract data records from search engine results pages which contain important information for computer users. Our WRAPPER consists of a series of data filter to detect and remove irrelevant data from the web page. In the filtering stages, we incorporate two main algorithms which are able to check the similarity of data records and to detect and extract the correct data region based on their component sizes. To evaluate the performance of our algorithm, we carry out experimental and deletion tests. Experimental tests show that our WRAPPER outperforms the existing state of the art WRAPPERs such as ViNT and DEPTA. Deletion studies by replacing our novel techniques with state of the art conventional techniques show that our WRAPPER design is efficient and could robustly extract data records from search engine results pages. With the speed advantages, our WRAPPER could be beneficial in processing large amount of web sites data, which could be helpful in meta search engine development.