Application of Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criterion in Imbalanced Data Integration Classification

Gao, Yuelin; Wang, Kaiguang; Gao, Chenyang; Shen, Yulong; Li, Teng

doi:10.3390/math7121237

Cited by 5 publications

(4 citation statements)

References 48 publications

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“…The performance of proposed VPPSO is compared with that of GASMOTE, 9 MPSO, 10 RSMOTE, 11 FFOS, 12 and DE-MPFSC. 17 Before implementation of VPPSO, the dataset has to be preprocessed to remove unwanted complexities during classification process. The missing values in the dataset are imputed using BACO which combines Bayesian classifier and Ant Colony optimization for imputing both discrete and continuous missing values.…”

Section: Implementation Results and Discussionmentioning

confidence: 99%

“…size) and number of samples of the majority class (Maj. size). The performance of proposed VPPSO is compared with that of GASMOTE, 9 MPSO, 10 RSMOTE, 11 FFOS, 12 and DE‐MPFSC 17 …”

Section: Implementation Results and Discussionmentioning

confidence: 99%

“…In Gao et al, 17 Differential Evolution Integration Algorithm Based on Mixed Penalty Function Screening Criteria (DEMPFSC) algorithm is introduced. The convergence nature of the proposed algorithm is proven by using Markov sequence and data integration is also maintained in this methodology.…”

Section: Introductionmentioning

confidence: 99%

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Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Priya¹,

Rajappan²,

Anitha³

et al. 2021

Computational Intelligence

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Real-world datasets used for classification often face many challenges when they are imbalanced in nature which is unavoidable and need to be handled by analysts. Many researchers have proposed methods for handling imbalanced datasets and they mostly concentrated on handling binary classification with only two class labels. Only very few research works have been carried out for treating highly imbalanced datasets and fail to handle multiclass datasets. To address imbalance problem in multiclass datasets, this paper proposes a Variable Population sized Particle Swarm Optimization (VPPSO) which is a modified version of Particle Swarm Optimization (PSO) which works based on clustering. PSO usually has fixed population size and has high computational complexity. In order to reduce this, the population size is varied over generations and the particles are loaded into the population iteratively by retaining the balance nature of solutions. PSO optimizes the selection of training and testing samples from each class label in imbalanced datasets for improved classification results. From the implementation results, it is evident that using VPPSO, highly imbalanced datasets with multiclass attributes are classified more efficiently

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Section: Implementation Results and Discussionmentioning

confidence: 99%

“…size) and number of samples of the majority class (Maj. size). The performance of proposed VPPSO is compared with that of GASMOTE, 9 MPSO, 10 RSMOTE, 11 FFOS, 12 and DE‐MPFSC 17 …”

Section: Implementation Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Priya¹,

Rajappan²,

Anitha³

et al. 2021

Computational Intelligence

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show abstract

“…Differential Evolution is a stochastic algorithm for a heuristic search for a global minimum using evolutionary operators [10], [1]. The Differential Evolution algorithm creates a new population 𝑄 by gradually creating a point 𝑦 for each point 𝑥 𝑖 , 𝑖 = 1, .…”

Section: Introductionmentioning

confidence: 99%

A survey on the global optimization problem using Kruskal–Wallis test

Ďuriš¹,

Tirpáková²

2020

AMI

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The article deals with experimental comparison and verification of stochastic algorithms for global optimization while searching the global optimum in dimensions 3 and 4 of selected testing functions in Matlab computing environment. To draw a comparison, we took the algorithms Controlled Random Search, Differential Evolution that we created for this test and implemented in Matlab, and fminsearch function which is directly built in Matlab. The basic quantities to compare algorithms were time complexity while searching the considered area and reliability of finding the global optimum of the 1st De Jong function, Rosenbrock's saddle, Ackley's function and Griewangk's function. The time complexity of the algorithms was determined by the number of test function evaluations during the global optimum search and we analysed the results of the experiment using the "Kruskal-Wallis test" non-parametric method.

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A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

Gao

Wang

et al. 2021

Complex Intell. Syst.

Self Cite

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In this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

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Application of Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criterion in Imbalanced Data Integration Classification

Cited by 5 publications

References 48 publications

Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

A survey on the global optimization problem using Kruskal–Wallis test

A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

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