Feature Selection Problem Research Articles

Benefiting From Single-Objective Feature Selection to Multiobjective Feature Selection: A Multiform Approach.

Evolutionary multiobjective feature selection (FS) has gained increasing attention in recent years. However, it still faces some challenges, for example, the frequently appeared duplicated solutions in either the search space or the objective space lead to the diversity loss of the population, and the huge search space results in the low search efficiency of the algorithm. Minimizing the number of selected features and maximizing the classification performance are two major objectives in FS. Usually, the fitness function of a single-objective FS problem linearly aggregates these two objectives through a weighted sum method. Given a predefined direction (weight) vector, the single-objective FS task can explore the specified direction or area extensively. Different direction vectors result in different search directions in the objective space. Motivated by this, this article proposes a multiform framework, which solves a multiobjective FS task combined with its auxiliary single-objective FS tasks in a multitask environment. By setting different direction vectors, promising feature subsets from single-objective FS tasks can be utilized, to boost the evolutionary search of the multiobjective FS task. By comparing with five classical and state-of-the-art multiobjective evolutionary algorithms, as well as four well-performing FS algorithms, the effectiveness and efficiency of the proposed method are verified via extensive experiments on 18 classification datasets. Furthermore, the effectiveness of the proposed method is also investigated in a noisy environment.

B-PSA: A Binary Pendulum Search Algorithm for the Feature Selection Problem

The digitization of information and technological advancements have enabled us to gather vast amounts of data from various domains, including but not limited to medicine, commerce, and mining. Machine learning techniques use this information to improve decision-making, but they have a big problem: they are very sensitive to data variation, so it is necessary to clean them to remove irrelevant and redundant information. This removal of information is known as the Feature Selection Problem. This work presents the Pendulum Search Algorithm applied to solve the Feature Selection Problem. As the Pendulum Search Algorithm is a metaheuristic designed for continuous optimization problems, a binarization process is performed using the Two-Step Technique. Preliminary results indicate that our proposal obtains competitive results when compared to other metaheuristics extracted from the literature, solving well-known benchmarks.

Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm.

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA's performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA's performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis.

A fuzzy transfer function based on the behavior of meta-heuristic algorithm and its application for high-dimensional feature selection problems

Many discrete optimization problems are in the class of NP-hard problems; therefore, exact algorithms cannot achieve an optimum solution in reasonable time. Approximate algorithms like binary meta-heuristic algorithms have provided good results in these problems. Since many meta-heuristic algorithms have been introduced for continuous search spaces, employing a transfer function is one of the simplest methods to map the search space to the binary one. Many transfer functions have been introduced, divided in atomic and compound transfer functions. In the atomic transfer functions, a single function is applied in the algorithm, such as S-shaped, U-shaped, V-shaped, Z-shaped and so on. Compound transfer functions employ more than one atomic transfer function in their structures, such as Mirrored Time-Varying S-shaped, χ-shaped, and Upgrade transfer functions. An improper transfer function decreases the algorithm's performance in the binary search space. Hence, several transfer functions are usually evaluated for solving a binary problem to select the best one. In this study, a fuzzy transfer function is proposed and applied in Binary Particle Swarm Optimization (FBPSO). The behavior of PSO is analyzed, and the fuzzy transfer function and its rules create a new binary position. The performance of atomic and compound transfer functions employed in BPSO and some recently introduced binary meta-heuristic algorithms are compared with FBPSO in high-dimensional feature selection problems. The experimental results show that FBPSO achieves the best solution in terms of minimum error and feature selection rate compared with other comparative algorithms.

An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets.

A new binary object-oriented programming optimization algorithm for solving high-dimensional feature selection problem

Feature selection (FS) is a crucial task in machine learning applications, which aims to select the most appropriate feature subset while maintaining high classification accuracy with the minimum number of selected features. Despite the widespread usage of metaheuristics as wrapper-based FS techniques, they show reduced effectiveness and increased computational cost when applied to high-dimensional datasets. This paper presents a novel Binary Object-Oriented Programming Optimization Algorithm (BOOPOA) for FS of high dimensional datasets, where the Object-Oriented Programming Optimization Algorithm (OOPOA) is a novel optimization technique inspired by the inheritance concept of Object-Oriented programming (OOP) languages. The effectiveness of this method in solving high dimensional FS problems is validated by using 26 datasets, most of which are of high dimension (large number of features). Seven existing FS algorithms are compared with the proposed OOPOA using various metrics, including best fitness, average fitness (AVG), selection size, and computational time. The results prove the superiority of the proposed algorithm over the other FS algorithms, having an average performance of %92.5, 0.078, 0.084, %38.9, and 8.6 min for classification accuracy, best fitness, average fitness, size reduction ratio, and computational time. The outcomes demonstrate the proposed FS approach's superiority over currently used methods.

Loose-to-strict Markov blanket learning algorithm for feature selection

The Markov blanket (MB) represents a crucial concept in a Bayesian network (BN) and is theoretically the optimal solution to the feature selection problem. Methods based on conditional independence (CI) tests are prevalent for MB discovery. Currently, the main challenge is how to improve both the efficiency and effectiveness of this type of method. In this paper, we propose a novel divide-and-conquer discovery algorithm, loose-to-strict MB (LSMB), to discover MBs faster while maintaining high accuracy. LSMB first discovers the approximate parent–child (PC) and spouse sets of a target variable via the loose CI test strategy, a constraint for the condition set of CI tests. Then, by strict CI tests, i.e., without constraint for the size of the condition set, LSMB first removes non-MB nodes in the discovered approximate PC set and then categorizes and removes non-MB nodes in the discovered approximate spouse set. Finally, LSMB combines the discovered sets to obtain the MB of the target. Experiments on benchmark BN datasets show that LSMB can improve the efficiency of MB discovery while maintaining higher accuracy than the state-of-the-art MB discovery algorithms, and experiments on real-world datasets demonstrate the excellent performance of LSMB in feature selection.

A Filter-Based Improved Multi-Objective Equilibrium Optimizer for Single-Label and Multi-Label Feature Selection Problem

Effectively reducing the dimensionality of big data and retaining its key information has been a research challenge. As an important step in data pre-processing, feature selection plays a critical role in reducing data size and increasing the overall value of the data. Many previous studies have focused on single-label feature selection, however, with the increasing variety of data types, the need for feature selection on multi-label data types has also arisen. Unlike single-labeled data, multi-labeled data with more combinations of classifications place higher demands on the capabilities of feature selection algorithms. In this paper, we propose a filter-based Multi-Objective Equilibrium Optimizer algorithm (MOEO-Smp) to solve the feature selection problem for both single-label and multi-label data. MOEO-Smp rates the optimization results of solutions and features based on four pairs of optimization principles, and builds three equilibrium pools to guide exploration and exploitation based on the total scores of solutions and features and the ranking of objective fitness values, respectively. Seven UCI single-label datasets and two Mulan multi-label datasets and one COVID-19 multi-label dataset are used to test the feature selection capability of MOEO-Smp, and the feature selection results are compared with 10 other state-of-the-art algorithms and evaluated using three and seven different metrics, respectively. Feature selection experiments and comparisons with the results in other literatures show that MOEO-Smp not only has the highest classification accuracy and excellent dimensionality reduction on single-labeled data, but also performs better on multi-label data in terms of Hamming loss, accuracy, dimensionality reduction, and so on.

Heart disease prediction (HDP) using Artificial Intelligence and IoMT for intelligent healthcare models

The present growth in Internet of Medical Things (IoMT) and Artificial Intelligence (AI) paved a way for advanced healthcare systems from conventional methods. The integration of AI and IoMT provides varied chances in medical domain. With that concern, the proposed model derives a novel model for Heart Disease Prediction (HDP), incorporates IoMT and AI. The proposed model comprises of different phases of functions, as, data collection, data preparation, feature optimization and selection, classification. IoMT devices include medical or wearable sensors are used for continuous collection of medical statistics while machine learning model process the data for disease prediction. Here, a new feature selection model called Enhanced Binary Particle Swarm Optimization (EBPSO) for reducing joint feature selection problems. With the extracted features, classification is performed with Cascaded Long Short Term Memory (CLSTM) model for attaining better accuracy of medical data classification. During evaluation, the proposed HDP model achieved the maximal accuracy in disease prediction. Hence, the model can be effectively used for diagnosing heart disease in Smart Healthcare Models.

An enhanced multimodal multi-objective genetic algorithm with a novel adaptive crossover mechanism for feature selection

Nowadays, multimodal multi-objective optimization problems (MMOPs) have received increasing attention from many researchers. In such problems, there are situations where two or more Pareto Sets (PSs) correspond to the same Pareto Front (PF). It is crucial to obtain as many PSs as possible without compromising the performance of the objective space. Therefore, this paper proposes an enhanced multimodal multi-objective genetic algorithm with a novel adaptive crossover mechanism, named AEDN_NSGAII. In the AEDN_NSGAII, the special crowding distance strategy can provide potential development opportunities for individuals with a larger crowding distance. An adaptive crossover mechanism is established by combining the simulated binary crossover (SBX) operator and the Laplace crossover (LP) operator, which adaptively improves the ability to obtain Pareto optimal solutions. Meanwhile, an elite selection mechanism can efficiently get more excellent individuals as parents to enhance the diversity of the decision space. Then, the proposed algorithm is evaluated on the CEC2019 test suite by the Friedman method and discussed for its feasibility through ablation experiments and boxplot analysis of PSP indicators. Experimental results show that AEDN_NSGAII can effectively search for more PSs without weakening the diversity and convergence of objective space. Finally, the performance of AEDN_NSGAII on the multimodal feature selection problem is compared with that of the other four algorithms. The statistical analysis demonstrates that the proposed algorithm has great potential for resolving this issue.

Review on Hybrid Swarm Algorithms for Feature Selection

Feature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedures, the majority of hybrid algorithms enhance classification accuracy.

An interactive feature selection method based on multi-step state transition algorithm for high-dimensional data

Feature selection (FS) has been extensively employed in classification tasks to reduce data dimensionality and enhance prediction performance effectively. Recently, hybrid filter-wrapper methods have exhibited promising results in FS problems by leveraging both advantages. However, the inadequate integration of the filter method into the wrapper method leads to the hybrid algorithms exhibiting poor efficiency in classification as datasets grow in complexity. In this paper, an interactive feature selection framework based on state transition algorithm (STA) is proposed to address high-dimensional FS problems. In this framework, prior knowledge of the features is formed via the mutual information-based filter method. The STA is a powerful search engine that traverses the feature space in the wrapper stage. Moreover, an external trainer with prior knowledge will guide the exploration direction of STA in a simple but efficient way to accelerate the search process. And a self-adaptive mechanism is proposed to adjust the prior knowledge during the search process. Specifically, the external trainer establishes the interactive loop, while the self-adaptive mechanism aims to feed feature information back to the loop. In addition, the search process is prevented from being trapped in local optima by employing a multi-step STA, which allows for continuous transformations of the solution in probability. Finally, the proposed FS method is applied to various public classification datasets. The experimental results demonstrate that the proposed method is a highly competitive FS method, outperforming several state-of-the-art algorithms in generating an optimal subset of features.

Reinforcement learning-based comprehensive learning grey wolf optimizer for feature selection

Turning heavy and high-dimensional raw data into knowledge for decision makers is a complex process. Feature selection (FS) can do this task well by removing irrelevant and/or redundant parts from the raw dataset, aiming at reducing dimensionality and improving accuracy. In this work, a reinforcement learning-based comprehensive learning grey wolf optimizer (RLCGWO) is designed to solve the FS problem, which is modeled as a combinatorial optimization problem. First, a comprehensive learning operator is proposed, containing the static and dynamic learning strategies. These two strategies provide GWO with the exploration capability in different ways. Second, a novel RL-based policy regulation technique is developed, which is based on the Q-learning framework. Individuals are considered as agents that obtain the state of the environment based on the state encoding technique. Meanwhile, agents select the most appropriate actions from the well-designed action set based on the information provided by the Q-table. Furthermore, agents update the stand-alone Q-table with rewards to provide themselves with more timely and accurate feedback. Third, a chaotic-based learning strategy is devised for leaders to improve the quality of the optimal solution. The comparison results of the proposed RLCGWO with six successful GWO variants and three typical algorithms on the benchmarks initially demonstrate its advantages in convergence speed and accuracy. The proposed RLCGWO is finally applied to the challenging FS problem. The comparison results with six popular algorithms on 15 UCI datasets and 3 real world high-dimensional datasets underscore its high adaptability and versatility. Taken together, the proposed RLCGWO is a promising technique for FS.

Feature Refinement Method Based on the Two-Stage Detection Framework for Similar Pest Detection in the Field.

Efficient pest identification and control is critical for ensuring food safety. Therefore, automatic detection of pests has high practical value for Integrated Pest Management (IPM). However, complex field environments and the similarity in appearance among pests can pose a significant challenge to the accurate identification of pests. In this paper, a feature refinement method designed for similar pest detection in the field based on the two-stage detection framework is proposed. Firstly, we designed a context feature enhancement module to enhance the feature expression ability of the network for different pests. Secondly, the adaptive feature fusion network was proposed to avoid the suboptimal problem of feature selection on a single scale. Finally, we designed a novel task separation network with different fusion features constructed for the classification task and the localization task. Our method was evaluated on the proposed dataset of similar pests named SimilarPest5 and achieved a mean average precision (mAP) of 72.7%, which was better than other advanced object detection methods.

BiOM: A framework for multimodal multiobjective optimization

Multimodal multiobjective evolutionary algorithms are expected to search for not only Pareto optimal front but also the corresponding multiple equivalent Pareto optimal sets. To this end, this paper designs a framework to ameliorate the performance of the existing multiobjective evolutionary algorithms (MOEAs) for solving multimodal multiobjective optimization problems (MMOPs). In this framework, an MMOP is transformed into a bi-objective optimization problem. The first objective is built by either decomposition-based method or indicator-based method in MOEAs to ensure the population convergence. A diversity indicator is another objective used to preserve the population diversity. Based on these two objectives, two diversity selection strategies are developed, which are responsible for balancing the diversity and the convergence in the decision and objective spaces, respectively, at the same time. According to the feedback information in the evolution, our framework adaptively selects one of them to pick out the promising individuals. Four variants based on our framework are built and are evaluated on 22 MMOPs benchmark functions. Six feature selection problems are also employed to test the performance of our framework. The experimental results confirm that our proposed framework performs well in all criteria.

Multi-objective metaheuristic optimization algorithms for wrapper-based feature selection: a literature survey

In the data mining and machine learning (ML) discipline, feature selection problem is considered among many researchers in the recent times. Feature selection process targets to minimize feature set number and maximize performance accuracy by identifying optimal features. Multiple objectives are considered while identifying the optimal feature hence multi-objective metaheuristic optimization algorithms (MOMOAs) are applied. In this study, literature review is performed MOMOAs-for solving wrapper feature selection problem (WFS). The literature review for solving WFS problem and discuss the challenges faced by the researchers in solving the feature selection problem. The literature review is performed on all relevant studies published in the last 12 years [2009-2022]. A detailed overview of the feature selection preliminaries, MOMOAs-WFS, role of the classifier in feature selection problem are presented. The outcome of this literature review is to highlight the existing works related to WFS problem using MOMOAs. Finally, the research areas for improvement are identified and emphasized for the scientists to survey in the field of MOMOAs.

Differential Evolution With Duplication Analysis for Feature Selection in Classification.

By selecting a small subset of relevant features, feature selection can reduce the dimensionality of the problem while maintaining or increasing the discriminating ability of the data. However, many existing feature selection approaches ignore the fact that there are multiple optimal solutions to a feature selection problem. Multiple feature subsets with different features selected can achieve very similar or the same classification accuracy. To search for multiple optimal feature subsets, a niching-based differential evolution (DE) method with duplication analysis is proposed. In the proposed method, the duplicated feature subsets in the population are modified by the proposed subset repairing scheme which can produce unique feature subsets. Second, the mutation operator in DE is improved, which uses both the niche and global information to produce promising feature subsets. Third, a new selection method considering the diversity among feature subsets is adopted to form a new population for the next-generation. In the experiments, the proposed method is compared with seven evolutionary feature selection algorithms and two typical feature selection methods on 18 datasets. The results show that the proposed algorithm achieves higher classification accuracy than the compared methods on most of the used datasets. Furthermore, the proposed method can find different feature subsets with very similar or the same classification accuracy.

An improved binary grey wolf optimizer for constrained engineering design problems

AbstractAn Improved binary Non‐Linear Convergent Bi‐phase Mutated Grey Wolf Optimizer (IbGWO) is proposed for solving feature selection problems with two main goals reducing irrelevant features and maximizing accuracy. We used stratified ‐fold cross‐validation that performs stratified sampling on the data to avoid overfitting problems. The fitness function used in the proposed algorithm allows choosing the solution with the minimum number of features if more than one feature has the same highest accuracy. When stratified cross‐validation is performed, the split datasets contain the same share of the feature of interest as the actual dataset. During stratified sampling, the cross‐validation result minimizes the generalization error to a considerable extent, with a smaller variance. Feature selection could be seen as an optimization problem that efficiently removes irrelevant data from high‐dimensional data to reduce computation time and improve learning accuracy. This paper proposes an improved Non‐Linear Convergent Bi‐Phase Mutated Binary Grey Wolf Optimizer (IbGWO) algorithm for feature selection. The bi‐phase mutation enhances the rate of exploitation of GWO, where the first mutation phase minimizes the number of features and the second phase adds more informative features for accurate feature selection. A non‐linear tangent trigonometric function is used for convergence to generalize better while handling heterogeneous data. To accelerate the global convergence speed, an inertia weight is added to control the position updating of the grey wolves. Feature‐weighted K‐Nearest Neighbor is used to enhance classification accuracy, where only relevant features are used for feature selection. Experimental results confirm that IbGWO outperforms other algorithms in terms of average accuracy of 0.8716, average number of chosen features of 6.13, average fitness of 0.1717, and average standard deviation of 0.0072 tested on different datasets and in terms of statistical analysis. IbGWO is also benchmarked using unimodal, multimodal, and IEEE CEC 2019 functions, where it outperforms other algorithms in most cases. Three classical engineering design problems are also solved using IbGWO, which significantly outperforms other algorithms. Moreover, the overtaking percentage of the proposed algorithm is .

A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT

The increasing trend toward using the Internet of Things (IoT) increased the number of intrusions and intruders annually. Hence, the integration, confidentiality, and access to digital resources would be threatened continually. The significance of security implementation in digital platforms and the need to design defensive systems to discover different intrusions made the researchers study updated and effective methods, such as Botnet Detection for IoT systems. Many problem space features and network behavior unpredictability made the Intrusion Detection System (IDS) the main problem in maintaining computer networks' security. Furthermore, many insignificant features have turned the feature selection (FS) problem into a vast IDS aspect. This paper introduces a novel binary multi-objective dynamic Harris Hawks Optimization (HHO) enhanced with mutation operator (MODHHO) and applies it to Botnet Detection in IoT. Afterward, the Feature Selection (FS) is undertaken, and the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Decision Tree (DT) classifiers are used to estimate the potential of the selected features in the precise detection of intrusions. The simulation results illustrated that the MODHHO algorithm performs well in Botnet Detection in IoT and is preferred to other approaches in its performance metrics. Besides, the computational complexity analysis results suggest that the MODHHO algorithm's overhead is more optimal than similar approaches. The MODHHO algorithm has performed better in comparison with other compared algorithms in all 5 data sets. In contrast with the machine learning methods of the proposed model in all five data sets, it has had a better error rate according to the AUC, G-mean, and TPR criteria. And according to the comparison made with filter-based methods, it has performed almost better in three datasets.

Copula entropy-based golden jackal optimization algorithm for high-dimensional feature selection problems

Feature selection (FS) is a crucial process that aims to remove unnecessary features from datasets. It plays a role in data mining and machine learning (ML) by reducing the risk associated with high-dimensional datasets. FS is considered a challenging problem that is difficult to solve efficiently due to its combinatorial nature. As the size of the problem increases, the computation time also grows. Recently, researchers have focused on metaheuristic FS algorithms specifically designed for high-dimensional datasets. Therefore, this article proposes a powerful metaheuristic algorithm called Binary Enhanced Golden Jackal Optimization (BEGJO), which is an improved version of the recently published Golden Jackal Optimization (GJO) algorithm. The original GJO algorithm faces challenges when dealing with high-dimensional FS problems, as it tends to get trapped in local optima. To address this issue, various enhancement strategies are employed to improve the efficiency of GJO. The proposed BEGJO algorithm utilizes Copula Entropy (CE) to reduce the dimensionality of high-dimensional FS problems while maintaining high classification accuracy using the K-Nearest Neighbour (K-NN) classifier. Additionally, four enhancement strategies are incorporated to enhance the exploration and exploitation capabilities of the fundamental GJO algorithm. The BEGJO algorithm is transformed into its binary form using the sigmoid transfer function, aligning it with the nature of the FS problem. It is then tested on various high-dimensional benchmark datasets. The effectiveness of BEGJO is evaluated by comparing it with well-known algorithms in terms of classification accuracy, feature dimension, and processing time. BEGJO outperforms other algorithms in terms of classification accuracy and feature dimension and ranks up to fourth in terms of processing time. Furthermore, the advantageous use of CE is demonstrated by comparing the performance of the proposed algorithm with traditional FS algorithms. Statistical evaluations are conducted to further validate the effectiveness and superiority of the proposed algorithm. The results confirm that BEGJO is an effective solution for high-dimensional FS problems.