An Improved Fish Swarm Algorithm for Neighborhood Rough Set Reduction and its Application

Zou, Li; Li, Hongxin; Jiang, Wei; Yang, Xinhua

doi:10.1109/access.2019.2926799

Cited by 21 publications

(11 citation statements)

References 34 publications

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“…Traina Jr. et al [20] indicated that most datasets have fractal features, and the fractal dimension is suitable as an evaluation criterion for feature selection. To eliminate redundant attributes and reduce the computational complexity, GA [26], [27], ACO [28], PSO [29], AFSA [30], [31] and Routing Algorithm [38]- [40] can be utilized as search strategies to improve computational efficiency of fractal dimension.…”

Section: Prediction Methods Based On Ibgso and Mfd For P2p Lendinmentioning

confidence: 99%

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A Novel Prediction Method Based on Improved Binary Glowworm Swarm Optimization and Multi-Fractal Dimension for P2P Lending Investment Risk

Xia

Zhu

et al. 2020

IEEE Access

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The frequent bankruptcy incidents of peer-to-peer (P2P) lending industry have damaged the benefits of investors in China, and how to accurately and efficiently predict the investment risks of P2P lending becomes an important problem. For this very reason, a novel prediction method based on improved binary glowworm swarm optimization and multi-fractal dimension (IBGSOMFD) for P2P lending investment risk is proposed. Firstly, we propose an improved binary glowworm swarm optimization, abbreviated IBGSO, by uniformly designing an initial population using the good-point set theory, improving the moving way of glowworms, and introducing the mechanism of population diffusion and variation. Secondly, IBGSO combined with multi-fractal dimension (MFD) is applied to feature selection, and the optimal subset extracted from the original dataset can be efficiently achieved utilizing IBGSO, which can reduce its redundant attributes, and retain its pivotal attributes of P2P lending investment risk. Finally, an investment risk prediction model of P2P lending based on support vector machine (SVM) is established, which can accurately and efficiently predict the investment risk of P2P lending. Experimental results on 6 University of California Irvine (UCI) benchmark datasets show that IBGSOMFD outperforms other stateof-the-art approaches in predictive ability and calculative efficiency, and its effectiveness and significance. After the performance verification of IBGSOMFD, this work looks at how it can be applied in the risk prediction of P2P lending investment in China to maintain a stable market order. INDEX TERMS Improved binary glowworm swarm optimization, multi-fractal dimension, P2P lending investment risk, prediction.

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Section: Prediction Methods Based On Ibgso and Mfd For P2p Lendinmentioning

confidence: 99%

“…With respect to searching strategy, heuristic algorithm is a good choice [23]. For instance, Genetic Algorithm (GA) [26], [27], Ant Colony Optimization (ACO) [28], Particle Swarm Optimization (PSO) [29], Artificial Fish Swarm Algorithm (AFSA) [30], [31]. However, all these methods have some defects.…”

Section: Introductionmentioning

confidence: 99%

A Novel Prediction Method Based on Improved Binary Glowworm Swarm Optimization and Multi-Fractal Dimension for P2P Lending Investment Risk

Xia

Zhu

et al. 2020

IEEE Access

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“…Fatigue test data are collected by references reading [31][32][33][34]. The total number of fatigue test samples of aluminum alloy welded joints is 75, N is the fatigue life of the welded joints denoted by stress cycle numbers before fatigue failure in the fatigue test of aluminum alloy welded joints.…”

Section: Fatigue Decision System Of Welded Jointsmentioning

confidence: 99%

An Optimization of Master S-N Curve Fitting Method Based on Improved Neighborhood Rough Set

Zou

Ren

et al. 2021

IEEE Access

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“…The AFSA is an intelligent optimization algorithm inspired by the behavior of fish swarms [25]. The algorithm has the advantages of a high robustness, good global convergence and low sensitivity to the initial value.…”

Section: Rogm-afsa-gvm Model a Gvm Optimized Based On The Artifmentioning

confidence: 99%

Hybrid Prediction Model for the Interindustry Carbon Emissions Transfer Network Based on the Grey Model and General Vector Machine

2020

IEEE Access

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Through analysis of the carbon emissions transfer network formed by the exchange of intermediate products among industries, we can promote the realization of national carbon emissions reduction goals. Therefore, it is of great significance to build a prediction model of the carbon emissions transfer network for more accurate predictions. According to the characteristics of the random oscillation sequence (ROS) of interindustry carbon emissions transfer, a hybrid prediction model denoted as the ROGM-AFSA-GVM is proposed based on the grey model (GM) for ROS and the general vector machine (GVM) optimized by the artificial fish swarm algorithm (AFSA). The proposed model uses the ROGM model to predict the general ROS trend and relies on the AFSA-GVM model to predict the nonlinear law of ROS. The predicted values of the two parts are combined to obtain predicted interindustry carbon emissions transfer values. The proposed model is used to simulate the interindustry carbon emissions transfer network of China. The simulation results show that the ROGM-AFSA-GVM model can effectively resolve the prediction problem of ROS. Comparing the predicted networks with the actually measured networks, it is verified that the proposed model is suitable for simulating the interindustry carbon emissions transfer network and has a good prediction performance. INDEX TERMS Hybrid prediction model, grey model, general vector machine, artificial fish swarm algorithm, carbon emissions transfer network.

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An Improved Fish Swarm Algorithm for Neighborhood Rough Set Reduction and its Application

Cited by 21 publications

References 34 publications

A Novel Prediction Method Based on Improved Binary Glowworm Swarm Optimization and Multi-Fractal Dimension for P2P Lending Investment Risk

A Novel Prediction Method Based on Improved Binary Glowworm Swarm Optimization and Multi-Fractal Dimension for P2P Lending Investment Risk

An Optimization of Master S-N Curve Fitting Method Based on Improved Neighborhood Rough Set

Hybrid Prediction Model for the Interindustry Carbon Emissions Transfer Network Based on the Grey Model and General Vector Machine

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