A Hybrid Discrete Imperialist Competition Algorithm for Gene Selection for Microarray Data

Aorigele,; Tang, Zheng; Todo, Yuki; Gao, Shangce

doi:10.2174/1570164614666171128152327

Cited by 7 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the usefulness of Trader in selecting informative features/genes, it was applied to the downloaded datasets (Table 1 ), and the outcomes were compared with four other public/effective optimization algorithms. These algorithms (i.e., WCC [ 27 ], LCA [ 31 ], PSO [ 32 ], and ICA [ 33 ]) were chosen because of their diversities and proper functionalities reported in the prior studies. Because the values of OAs’ parameters strongly affected their efficiencies, a trial–error method was employed to regulate them [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

A voting-based machine learning approach for classifying biological and clinical datasets

2023

View full text Add to dashboard Cite

Background Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods. Results The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value < 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure. Conclusion Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans.

show abstract

Section: Resultsmentioning

confidence: 99%

A voting-based machine learning approach for classifying biological and clinical datasets

2023

View full text Add to dashboard Cite

show abstract

“…In order to prevent the algorithm from falling into local optimization, we set up the historical information layer to manage the elite top layer by Eqs. ( 12) and (13). Besides the update of elite top layer, the historical information layer is also evolved.…”

Section: B Description Of Mgsamentioning

confidence: 99%

“…Physics-inspired algorithms include gravitational search algorithm (GSA) [9], simulated annealing [10] and quantum computing [11]. Sociology-inspired algorithms contain brain storm optimization [12], imperialist competitive algorithm [13] and memetic algorithms [14]. These MHAs have been widely used in various optimization problems such as mobile edge computing [15]- [17] and controllers [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Distributed Gravitational Search Algorithm With Multi-Layered Information Interaction

Yang

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

Population structures play a crucial role in individuals' evolution. Gravitational search algorithm (GSA) inspired by physical laws is a population-based algorithm. Its population structure is able to influence the individuals' search behavior. In this paper, we propose a distributed GSA with multi-layered information interaction, termed as MGSA, to offer a good balance between exploitation and exploration. A historical information layer and an elite top layer are designed to improve individuals' interaction. A distributed structure maintains the population diversity. Experimental results on CEC2017 benchmark functions and a real-world static economic dispatch problem confirm the effectiveness of MGSA in comparison with several state-of-the-art algorithms. In addition, landscape search trajectory and population diversity analyses verify the excellent convergence of MGSA.

show abstract

“…Representative bio-inspired algorithms include genetic algorithms [4], evolutionary strategies [5], differential evolution (DE) [6]- [8], spherical evolution [9], artificial immune algorithms [10], particle swarm optimization (PSO) [11], ant colony optimization [12], etc. Physics-inspired algorithms consist of simulated annealing [13], gravitational search algorithm [14], and quantum computing [15], while sociology-inspired ones usually denote imperialist competitive algorithm [16], brain storm optimization [17], culture algorithm [18], memetic algorithms [19], and so on. More importantly, these MHAs have been widely applied on various practical problems, from engineering [20], [21] to bio-informatics [22], [23], and achieved great successes in comparison with traditional mathematical analysis methods as they can obtain an acceptable solution with reasonable computational burden [24]- [28].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Yang

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

show abstract

A Hybrid Discrete Imperialist Competition Algorithm for Gene Selection for Microarray Data

Cited by 7 publications

References 0 publications

A voting-based machine learning approach for classifying biological and clinical datasets

A voting-based machine learning approach for classifying biological and clinical datasets

A Novel Distributed Gravitational Search Algorithm With Multi-Layered Information Interaction

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Contact Info

Product

Resources

About