Efficient Selection Algorithm Research Articles

An efficient instance selection algorithm for fast training of support vector machine for cross-project software defect prediction pairs

SVM is limited in its use for cross-project software defect prediction because of its very slow training process. So, this research article proposes a new instance selection (IS) algorithm called boundary detection among classes (BDAC) to reduce the training dataset size for faster training of SVM without degrading the prediction performance. The proposed algorithm is evaluated against six existing IS algorithms based on accuracy, running time, data reduction rate, etc. using 23 general datasets, 18 software defect prediction datasets, and two shape-based datasets, and results prove that BDAC is better than the selected algorithm based on collective comparison.

Retraction notice: Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Retraction notice: Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

A novel approach to mitigate academic underachievement in higher education: Feature selection, classifier performance, and interpretability in predicting student performance

The main goal of this study is to address the ongoing problem of low academic performance in higher education by using machine learning techniques. We use a dataset from a higher education institution that includes various information available at student enrollment, such as academic history, demographics, and socio-economic factors. To address this issue, we introduce a new method that combines the Slime Mould Algorithm (SMA) for efficient feature selection with a Forest-Optimized Neural Network (FO-NN) Classifier. Our method aims to identify students at risk of academic failure early. Using the SMA, we simplify the feature selection process, identifying important attributes for accurate predictions. The Forest Optimization technique improves the classification process by optimizing the neural network model. The experimental results of this study show that our proposed method is effective, with significant improvements in feature selection accuracy and notable enhancements in the predictive performance of the neural network classifier. By selecting a subset of relevant features, our approach deals with high-dimensional datasets and greatly improves the quality and interpretability of predictive models. The innovative combination of the SMA and the FO-NN classifier increases accuracy, interpretability, and the ability to generalize in predicting student performance. This work contributes to a more effective strategy for reducing academic underachievement in higher education.

Improved Binary Meerkat Optimization Algorithm for efficient feature selection of supervised learning classification

Feature selection (FS) is a crucial step in machine learning and data mining projects. It aims to remove redundant and uncorrelated features, thus improving the accuracy of models. However, it can be challenging to select the optimal features, especially for datasets with many features. This paper proposes the Binary Meerkat Optimization Algorithm (BMOA) to address the issue of FS. The BMOA selects the most related and useful features. Additionally, an improved BMOA (IBMOA) is introduced, which enhances exploration and exploitation capabilities by incorporating the Periodic Mode Boundary Handling (PMBH) strategy and the Local Search (LS) process. This helps to reduce dimensionality and enhance classification accuracy. To evaluate the significance of selected features, two widely used classifiers, namely k-nearest Neighbor (k-NN) and Support Vector Machine (SVM), were used as quality raters. The proposed IBMOA and the original BMOA were compared on 21 multi-scale and multi-faceted benchmark datasets. The binary structures of eight contemporary algorithms, including Binary Artificial Bee Colony (BABC), Binary Grey Wolf Optimization (BGWO), Binary Sailfish Optimizer (BSFO), Binary Particle Swarm Optimizer (BPSO), Binary Bird Swarm Algorithm (BBSA), Binary Whale Optimization Algorithm (BWOA), Binary Grasshopper Optimization Algorithm (BGOA), and Binary Bat Algorithm (BBA), were also analyzed and compared. The proposed IBMOA algorithm outperforms competitors on small and large datasets, obtaining optimal solutions within a reasonable timeframe. This is true for the proposed IBMOA, which has been statistically proven to be highly competitive using the Wilcoxon rank sum test (with alpha=0.05).

Local-environment-guided selection of atomic structures for the development of machine-learning potentials.

Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs.

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

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.

Matrix-based vs. vector-based linear discriminant analysis: A comparison of regularized variants on multivariate time series data

Over the past two decades, matrix-based or bilinear discriminant analysis (BLDA) methods have received much attention. However, it has been reported that the traditional vector-based regularized LDA (RLDA) is still quite competitive and could outperform BLDA on some benchmark datasets. A central question is whether the superiority of the vector-based RLDA would always hold for general matrix data, or is there any type of matrix data on which BLDA would perform better than RLDA? Actually, the reported comparisons are found to suffer from two limitations: (i) the comparisons are only limited to image data, and (ii) regularized RLDA is compared with non-regularized BLDA. In this paper, we break the two limitations and investigate the central question on another type of matrix data, namely multivariate time series (MTS) data. We propose a new two-parameter regularized BLDA (RBLDA) for MTS data classification. To choose the two parameters, we develop an efficient model selection algorithm. The newly proposed RBLDA enables us to perform a fair comparison between vector-based RLDA and matrix-based RBLDA. Experiments on a number of real MTS data sets are conducted to compare RBLDA with RLDA and evaluate the proposed algorithm. The results reveal that the superiority of the vector-based RLDA does not always hold for general matrix data, and RBLDA outperforms RLDA on MTS data. Moreover, the proposed model selection algorithm is efficient, and RBLDA can produce better visualization of MTS data than RLDA.

Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization

Wireless sensor networks (WSNs) are typically made up of small, low-power sensor nodes (SNs) equipped with capability for wireless communication, processing, and sensing. These nodes collaborate with each other to form a self-organizing network. They can collect data from their surrounding environment, such as temperature, humidity, light intensity, or motion, and transmit it to a central base station (BS) or gateway for additional processing and analysis. LEACH and TSEP are examples of cluster-based protocols developed for WSNs. These protocols require careful design and optimization of CH selection algorithms, considering factors such as energy consumption, network scalability, data aggregation, load balancing, fault tolerance, and adaptability to dynamic network conditions. Various research efforts have been made to develop efficient CH selection algorithms in WSNs, considering these challenges and trade-offs. In this paper, the Grey Wolf Optimization (GWO) algorithm is employed to address the problem of selecting CHs (CHs) in WSNs. The proposed approach takes into account two parameters: Residual Energy (RE) and the distance of node (DS)s from the BS. By visualizing and analyzing the GWO algorithm under variable parameters in WSNs, this research identifies the most appropriate node from all normal nodes for CH selection. The experimental results demonstrate that the proposed model, utilizing GWO, outperforms other approaches in terms of performance.

A greedy Galerkin method to efficiently select sensors for linear dynamical systems

A key challenge in inverse problems is the selection of sensors to gather the most effective data. In this paper, we consider the problem of inferring the initial condition to a linear dynamical system and develop an efficient control-theoretical approach for greedily selecting sensors. Our method employs a Galerkin projection to reduce the size of the inverse problem, resulting in a computationally efficient algorithm for sensor selection. As a byproduct of our algorithm, we obtain a preconditioner for the inverse problem that enables the rapid recovery of the initial condition. We analyze the theoretical performance of our greedy sensor selection algorithm as well as the performance of the associated preconditioner. Finally, we verify our theoretical results on various inverse problems involving partial differential equations.

Improved Butterfly Optimization Algorithm for Energy Efficient Antenna Selection Over Wireless Cellular Networks

Improved Butterfly Optimization Algorithm for Energy Efficient Antenna Selection Over Wireless Cellular Networks

A Performance Analysis of Massive MIMO System using Antenna Selection Algorithms

A large number of transmitting components makes Massive Multiple-Input Multiple-Output (MIMO) one of the most hopeful solution for the 5G technology. However, a large antenna system boosts the hardware intricacy and cost of the system because of RF transceivers used at the base station for every antenna element. Hence, antenna selection is one of the most effective schemes to select a good subset of antennas with the finest channel circumstances and contribute maximum to the channel capacity. This paper presents Branch and Bound (BAB) algorithm for efficient antenna selection in Massive MIMO technology. The effectiveness of the simulated BAB algorithm is evaluated based on channel capacity and compared with the traditional state of arts such as fast antenna selection algorithm, Exhaustive Search, Fast antenna selection, CBF, CBW, Random antenna selection, etc. Sunflower Optimization-based antenna selection has been shown to provide improved results in terms of channel capacity when compared to the traditional Branch and Bound algorithm. The results indicate that the Sunflower Optimization technique is a promising alternative for antenna selection in Massive MIMO systems, especially in cases where a large number of antennas are present at the transmitter and receiver ends. The proposed solution provides significant improvements over the traditional methods, making it an attractive option for optimizing MIMO performance in future wireless communication systems.

Developing a Robust Emergency Information System for Natural Disasters

A lot of Emergency Information Systems have been developed to inform people about impending emergencies. Most of them are part of an integrated system, collaborating with a respective warning system. No matter which technology is employed in the development process, an Emergency Information System aspires to deliver emergency content accurately and rapidly. Most of Emergency Information Systems transmit the emergency content in a predetermined geographical area. In parallel, a typical DRM Emergency Information System doesn’t vouch for transmission accuracy in the case of a possible communication loss or a power outage. This paper presents a DRM-based Emergency Information System that works well in remote areas with rough geographical terrain, offsetting a possible communication loss or a possible power outage. An efficient broadcast selection algorithm has been developed to answer this purpose. The paper also indicates the maximum coverage of the underlying system, in a mountainous area (Vigla), taking advantage of a standard methodology called " LEGBAC”. Specific software was used to come up with the wide area coverage map. The results proved that our system achieved maximum coverage in any antenna calibration (99% approximately) and the value of the respective signal strength was not significantly altered by the increase in the number of kilometers away from the transmission center, indicating the robustness of our system and the competency of the broadcast selection algorithm.

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

Objective. The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as ‘spikes’, in people with epilepsy. Approach. A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen’s kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results. Main results. We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise 300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG. Significance. These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.

Discrete clusters formulation through the exploitation of optimized k-modes algorithm for hypotheses validation in social work research: the case of greek social workers working with refugees

This article focuses on the results of self-funded quantitative research conducted by social workers working in the “refugee” crisis and social services in Greece (1). The research, among other findings, argues that front-line professionals possess specific characteristics regarding their working profile. Statistical methods in the research performed significance tests to validate the initial hypotheses concerning the correlation between dataset variables. On the contrary of this concept, in this work, we present an alternative approach for validating initial hypotheses through the exploitation of clustering algorithms. Toward that goal, we evaluated several frequently used clustering algorithms regarding their efficiency in feature selection processes, and we finally propose a modified k-Modes algorithm for efficient feature subset selection.

Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms

Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.

Optimal Scale Combination Selection Integrating Three-Way Decision With Hasse Diagram.

Multi-scale decision system (MDS) is an effective tool to describe hierarchical data in machine learning. Optimal scale combination (OSC) selection and attribute reduction are two key issues related to knowledge discovery in MDSs. However, searching for all OSCs may result in a combinatorial explosion, and the existing approaches typically incur excessive time consumption. In this study, searching for all OSCs is considered as an optimization problem with the scale space as the search space. Accordingly, a sequential three-way decision model of the scale space is established to reduce the search space by integrating three-way decision with the Hasse diagram. First, a novel scale combination is proposed to perform scale selection and attribute reduction simultaneously, and then an extended stepwise optimal scale selection (ESOSS) method is introduced to quickly search for a single local OSC on a subset of the scale space. Second, based on the obtained local OSCs, a sequential three-way decision model of the scale space is established to divide the search space into three pair-wise disjoint regions, namely the positive, negative, and boundary regions. The boundary region is regarded as a new search space, and it can be proved that a local OSC on the boundary region is also a global OSC. Therefore, all OSCs of a given MDS can be obtained by searching for the local OSCs on the boundary regions in a step-by-step manner. Finally, according to the properties of the Hasse diagram, a formula for calculating the maximal elements of a given boundary region is provided to alleviate space complexity. Accordingly, an efficient OSC selection algorithm is proposed to improve the efficiency of searching for all OSCs by reducing the search space. The experimental results demonstrate that the proposed method can significantly reduce computational time.

FOC-MOP: Fuzzy Optimal Clustering Based Multi-Objective Parameter Route Selection for Energy Efficiency

Wireless sensor networks are emerging and involved in a wide range of applications. Improving energy efficiency is a significant task in WSNs. Energy consumption is a crucial problem that affects the network lifetime due to energy limitations in sensor nodes. So, it's required to introduce more energy-efficient protocols for WSNs. Different types of energy-efficient solutions exist for WSNs. The energy consumption is optimized and controlled if clustering is an efficient data collection method. The cluster heads aggregate the data from the member nodes and transmit the aggregated data to BS (base station) either in an uncompressed or compressed manner after collecting all data by the cluster-heads (CHs). Due to the heavy inter-cluster relay and aggregation Overhead, earlier death happens to the CHs located very close to the sink. There is a need for an efficient and reliable CH selection algorithms to select reliable CHs to prevent the earlier death of CHs and frequent reselection of CHs. Therefore, in this work, a fuzzy optimal CH selection algorithm is proposed to select the optimal CHs to improve the network's lifetime. The proposed algorithm uses the following multi-objective parameters: cluster load, communication cost, neighbour density, inter and intra-cluster distance, and node energy for CH selection. The modified Emperor Penguin Optimization (EPO) is used in the proposed work to determine an optimal route for transmitting the data from CH to sink. The modified EPO algorithm helps to improve the multi-hop routing efficiency, reduces the cost of long-distance transmission, and decreases energy utilization during data transmission. The modified EPO considers the parameters like link quality factor, relative distance factor, and residual energy factor for relay selection. The proposed technique was compared against earlier methods like Z-DSS, CCR, RETOPSIS, and LEACH-FC. The various parameters like packet delivery ratio, lifetime, energy consumption, end-to-end delay, and Throughput are studied. The evaluation results prove that the proposed mechanism achieves 40% energy efficiency with 60% lesser Overhead and improves the Throughput rate by 50% than the compared methods.

A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection

A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection

Hybrid slime mould - Grey wolf optimization algorithm for efficient feature selection

Selection of features is an effective method for minimizing the amount of data features in order to improve machine learning classification performance. Choosing a set of attributes is a high-level procedure for selecting a collection of relevant features. To boost the classifier's performance, use a dimensional dataset. We outline a typical feature selection problem in this work in order to minimize the amount of role and responsibilities while improving accuracy. Different classification dataset from the Machine learning repository have been used to test SMA with GWO as a feature selection strategy. The feature selections for UCI repository datasets include Bat optimization, Cuckoo search optimization, Slime mould optimization, Whale optimization, Particle swarm optimization, and Grey wolf optimization.