Adaptive multi-objective swarm fusion for imbalanced data classification

Li, Jinyan; Fong, Simon; Wong, Raymond K.; Chu, Victor W.

doi:10.1016/j.inffus.2017.03.007

Cited by 79 publications

(23 citation statements)

References 39 publications

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“…Tables 1 to 3 record the accuracy, dimension (%) and kappa statistics 35 , 36 of the selected sub-datasets with different methods. Tables 4 and 5 present the precision and recall values, respectively, to help us evaluate and compare these methods.…”

Section: Resultsmentioning

confidence: 99%

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets

Li¹,

Fong²,

Wong

et al. 2017

Sci Rep

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Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, ‘It is not the strongest of the species that survives, but the most adaptable’. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.

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Section: Resultsmentioning

confidence: 99%

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets

Li¹,

Fong²,

Wong

et al. 2017

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…(1) Initialization according Equation (4) (4); (6) Record the best solution; (7) Estimate whether this meets the termination conditions. If so, output the optimal solution, otherwise goto step 2.…”

Section: The Proposed Algorithm Flow and Complexity Analysismentioning

confidence: 99%

“…Traditional optimization algorithms, such as Newton's method and the gradient descent method [2], can solve the simple and continuous differentiable function [3]. For complex, nonlinear, non-convex or discrete optimization problems, traditional optimization algorithms have a hard time finding a solution [4,5]. Using a swarm intelligence algorithm, such as the particle swarm optimization (PSO) algorithm [6] and artificial bee colony (ABC) algorithm [7], can find a more satisfactory solution.…”

Section: Introductionmentioning

confidence: 99%

Artificial Flora (AF) Optimization Algorithm

Cheng

Yan

2018

Applied Sciences

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Featured Application: The proposed algorithm can be used in unconstrained multivariate function optimization problems, multi-objective optimization problems and combinatorial optimization problems.Abstract: Inspired by the process of migration and reproduction of flora, this paper proposes a novel artificial flora (AF) algorithm. This algorithm can be used to solve some complex, non-linear, discrete optimization problems. Although a plant cannot move, it can spread seeds within a certain range to let offspring to find the most suitable environment. The stochastic process is easy to copy, and the spreading space is vast; therefore, it is suitable for applying in intelligent optimization algorithm. First, the algorithm randomly generates the original plant, including its position and the propagation distance. Then, the position and the propagation distance of the original plant as parameters are substituted in the propagation function to generate offspring plants. Finally, the optimal offspring is selected as a new original plant through the selection function. The previous original plant becomes the former plant. The iteration continues until we find out optimal solution. In this paper, six classical evaluation functions are used as the benchmark functions. The simulation results show that proposed algorithm has high accuracy and stability compared with the classical particle swarm optimization and artificial bee colony algorithm.

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“…As the objective function, the program can calculate the corresponding fitness of position X i of each particle, where the speed of the i th particle is V i = ( V i 1 , V i 2 , …, V iD ) T , the extremum value of each agent is P i = ( P i 1 , P i 2 , …, P iD ) T and the extremum of the population is P g = ( P g 1 , P g 2 , …, P gD ) T . In the process of iteration, the extremum value of each agent and the population will update their position and speed [ 16 ]. Eqs 2 and 3 show the mathematical process as follows: …”

Section: Related Workmentioning

confidence: 99%

Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data

Liu

Fong

et al. 2017

PLoS ONE

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Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performance improvements obtained by the former methods are not scalable to larger data scales. The latter methods, which we call Adaptive Swarm Balancing Algorithms, lead to significant efficiency and effectiveness improvements on large datasets while the first method is invalid. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. The proposed methods lead to more credible performances of the classifier, and shortening the run time compared to brute-force method.

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Adaptive multi-objective swarm fusion for imbalanced data classification

Cited by 79 publications

References 39 publications

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets

Artificial Flora (AF) Optimization Algorithm

Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data

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