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Background: The main goal of successful gene selection for microarray data is to find compact and predictive gene subsets which could improve the accuracy. Though a large pool of available methods exists, selecting the optimal gene subset for accurate classification is still very challenging for the diagnosis and treatment of cancer., Results: To obtain the most predictive genes subsets without filtering out critical genes, a gene selection method based on least absolute shrinkage and selection operator (LASSO) and an improved binary particle swarm optimization (BPSO) is proposed in this paper. To avoid overfitting of LASSO, the initial gene pool is divided into clusters based on their structure. LASSO is then employed to select high predictive genes and further calculate the contribution value which indicates the genes' sensitivity to samples' classes. With the second-level gene pool established by double filter strategy, the BPSO encoding the contribution information obtained from LASSO is improved to perform gene selection. Moreover, from the perspective of the bit change probability, a new mapping function is defined to guide the updating of the particle to select the more predictive genes in the improved BPSO., Conclusions: With the compact gene pool obtained by double filter strategies, the improved BPSO could select the optimal gene subsets with high probability. The experimental results on several public microarray data with extreme learning machine verify the effectiveness of the proposed method compared to the relevant methods.

Traditional gene selection methods for microarray data mainly considered the features' relevance by evaluating their utility for achieving accurate predication or exploiting data variance and distribution, and the selected genes were usually poorly explicable. To improve the interpretability of the selected genes as well as prediction accuracy, an improved gene selection method based on binary particle swarm optimization (BPSO) and prior information is proposed in this paper. In the proposed method, BPSO encoding gene-to-class sensitivity (GCS) information is used to perform gene selection. The gene-to-class sensitivity information, extracted from the samples by extreme learning machine (ELM), is encoded into the selection process in four aspects: initializing particles, updating the particles, modifying maximum velocity, and adopting mutation operation adaptively. Constrained by the gene-to-class sensitivity information, the new method can select functional gene subsets which are significantly sensitive to the samples' classes. With the few discriminative genes selected by the proposed method, ELM, K-nearest neighbor and support vector machine classifiers achieve much high prediction accuracy on five public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system., Competing Interests: The authors have declared that no competing interests exist.

Feature Selection (FS) is choosing a subcategory of features purposed to construct a machine learning model. Among the copious existing FS algorithms, Binary Particle Swarm Optimization Algorithm (BPSO) is prevalent with applications in several domains. However, BPSO suffers from premature convergence that affects exploration, resulting in dilapidation. In this current work, we boost the exploration of BPSO, incorporating the intelligence of crows to hide their food sources from other crows and predators and maintain diversity by implementing a clustering strategy. The clustering technique guarantees that the starting population is evenly distributed over the feature space while including more promising features. Additionally, suppose a crow realizes another crow or a predator is tracking it. In that case, the crow moves randomly to evict the stalker, leading to a better exploration of unexplored regions within the search space. We named the proposed method Hybrid Particle Swarm Optimization and Crow Search Algorithm with clustering initialization strategy (HPSOCSA-CIS). To evaluate the performance of HPSOCSA-CIS, 15 standard UCI datasets are utilized, and the outcomes are compared with recently proposed hybrid and standard optimization algorithms. From observation, HPSOCSA-CIS outperforms the comparing approaches for feature selection challenges on representative datasets that fall in the three-category based on dimensions. The HPSOCSA-CIS improves performance in terms of mean classification accuracy by 8.87%, 17.5%, and 21.90% on Low, medium, and high dimensional datasets, respectively. [ABSTRACT FROM AUTHOR]

Multi-leader comprehensive learning particle swarm optimiser possesses strong exploitation ability, by randomly selecting and assigning best-ranked particles as leaders during optimisation. However, it lacks the ability to preserve diversity by mainly focusing on exploitation, and adopting random selection to choose leaders also hinders its performance. To overcome these deficiencies, an improved multi-leader comprehensive learning particle swarm optimiser is proposed based on Karush-Kuhn-Tucker proximity measure and Gravitational Search Algorithm. Karush-Kuhn-Tucker proximity measure is employed to determine the best-ranked particles' contribution to the swarm's convergence to influence their selection as guides for other particles. Gravitational Search Algorithm is introduced to preserve the algorithm's ability to maintain diversity. To curb premature convergence and particles getting trapped in a local optimum, an adaptive reset velocity strategy is incorporated to activate stagnated particles. Some benchmark test functions are employed to compare the proposed algorithm with seven other peer algorithms. The results verify that our proposed algorithm possesses a better capability to elude local optima with faster convergence than other algorithms. Furthermore, to prove the efficacy of the application of our proposed algorithm in real-life, the algorithms are used to train a Feedforward neural network for epilepsy detection, of which our proposed algorithm outperforms the others. [ABSTRACT FROM AUTHOR]

Class topper optimization (CTO) algorithm divides the initial swarm into several sub-swarms, and this causes it to possess a strong exploration ability throughout optimization. It however randomly selects best-ranked particles as section toppers (ST's) and class topper (CT), and the inability of every particle to directly learn from the CT causes slow convergence during the latter stages of iterations. To overcome the algorithm's deficiency and find a good balance between exploration and exploitation, this study proposes an enhanced CTO (ECTPSO) based on the social learning characteristics of particle swarm optimization (PSO). We created an external archive called the assertive repository (AR) to store best-ranked particles and employed the Karush-Kuhn-Tucker (KKT) proximity measure to assist in the selection of STs and CT. Also, the intensive crowded sorting (ICS) is developed to truncate the AR when it exceeds its maximum size limit. To further encourage exploitation and avert particles from getting trapped in local optimum, we incorporated an adaptive performance adjustment strategy (APA) into our framework to activate particles when they are stagnated. The CEC2017 test suite is employed to evaluate the effectiveness of the proposed algorithm and four other benchmark peer algorithms. The results show that our proposed method possesses a better capability to elude local optima with faster convergence than the other peer algorithms. Furthermore, the algorithms were applied to economic load dispatch (ELD), of which our proposed algorithm demonstrated its effectiveness and competitiveness to address optimization problems. [ABSTRACT FROM AUTHOR]

The diversity of the population is a key factor for particle swarm optimisation (PSO) when dealing with most optimisation problems. The best previously visited positions of each particle are the exemplar in PSO to guide particle swarm to search, and the diversity of the population can be controlled by these best previously visited positions. Base on this idea of to control the diversity of population to improve the performance of PSO, this paper proposes a novel PSO with mutation breeding (MBPSO), which performs a mutation breeding operation periodically, to control the diversity of the population to improve the global optimisation ability. The mutation breeding operation can be divided into two steps: breeding and mutation. The breeding step is to replace all of best previously visited positions of each particle with the global best previously visited position, and the mutation step is to perform a mutation operation for those new generated best previously visited positions. In addition, we adopt a new updating mechanism of the global best previously position to avoid falling into local optimum. The experimental results on a suit of benchmark functions verifies that the proposed PSO is a competitive algorithm when compare with other PSO variants. [ABSTRACT FROM AUTHOR]

In real life, object detection is widely applied and plays a significant part in the field of computer vision. However, when detecting small objects, the advanced You Only Look Once v4 (YOLO v4) model often misses or incorrectly detects them due to the limited resolution and unclear features of the small objects, which reduces the detection accuracy. A small object detection algorithm based on YOLO v4 and Multi-scale Contextual information and Soft-CIOU loss function, called MCS-YOLO v4, is proposed in this paper. MCS-YOLO v4 adds a scale detection to the existing three scales to obtain rich location information. To enhance the ability of network to locate and classify the object, MCS-YOLO v4 introduces an expanded field-of-perception block. This block obtains the object contextual features and integrates them with the convolutional features to obtain more robust and discriminative features. To reduce the influence of insignificant information in images on small object, the attention module is introduced in the neck part of YOLO v4. To further improve the detection accuracy of small objects, the Soft-CIOU loss function is proposed. The aspect ratio weight factor is introduced into the weight function of the CIOU (Complete-IOU) loss function, while the Euclidean distance is subjected to the open-root operation, which improves the contribution of small objects to the loss function and enhances the learning ability of the network for small objects. The experimental results on the publicly available small object datasets verify that the proposed model has better detection effect than other detection models in detecting small objects. [ABSTRACT FROM AUTHOR]

To obtain predictive genes with lower redundancy and better interpretability, a hybrid gene selection method encoding prior information is proposed in this paper. To begin with, the prior information referred to as gene-to-class sensitivity (GCS) of all genes from microarray data is exploited by a single hidden layered feedforward neural network (SLFN). Then, to select more representative and lower redundant genes, all genes are grouped into some clusters by K-means method, and some low sensitive genes are filtered out according to their GCS values. Finally, a modified binary particle swarm optimization (BPSO) encoding the GCS information is proposed to perform further gene selection from the remainder genes. For considering the GCS information, the proposed method selects those genes highly correlated to sample classes. Thus, the low redundant gene subsets obtained by the proposed method also contribute to improve classification accuracy on microarray data. The experiments results on some open microarray data verify the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]