Wavelet twin support vector machines based on glowworm swarm optimization

Ding, Shifei; An, Yuexuan; Zhang, Xiekai; Wu, Fulin; Xue, Yu

doi:10.1016/j.neucom.2016.11.026

Cited by 60 publications

(18 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SI applications in those two aspects are mainly related to parameter tuning. For classification, works can be found in literature that combine SI algorithms with regression model [ 53 ], support vector machine [ 7 , 14 , 60 ], k-nearest neighbor classifiers [ 58 , 65 ], Decision trees [ 3 , 35 ], as well as the neural networks [ 30 , 62 ]. For clustering, some recent works are related to utilizing SI with k-means [ 28 , 59 , 61 ], c-means [ 21 ], and other linear or non-linear clustering algorithms [ 19 , 27 ].…”

Section: Theoretical Applicationsmentioning

confidence: 99%

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

Yang

Shen

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.

show abstract

Section: Theoretical Applicationsmentioning

confidence: 99%

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

Yang

Shen

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…PSO is a searching model based on the random orbit and it is likely to have a tendency to converge towards local optima or even arbitrary points rather than the global optimum. Glowworm swarm optimisation (GSO) [13] has the advantages of strong versatility and generality. On the basis of the PSO, in 2004, QPSO was proposed by Sun et al [14] PSO is easy to fall into the local optimal solution, so the search result is not necessarily the global optimal solution [15].…”

Section: Basic Theorymentioning

confidence: 99%

Research on fingerprint classification based on twin support vector machine

Ding

Shi

Jia

2020

IET Image Processing

Self Cite

View full text Add to dashboard Cite

“…where c 1 ≥ 0 and c 2 ≥ 0 are penalty parameters, ξ 1 and ξ 2 are slack variables, e 1 and e 2 are vectors with each element of the value of 1, s 1 and s 2 represent fuzzy membership of each type of sample points. By introducing the method of Lagrangian multipliers, the corresponding Wolfe dual of QPPs (18) and (19) can be represented as…”

Section: B Tbsvmmentioning

confidence: 99%

“…It makes training speed of TWSVM four times faster than SVM. From then on, various TWSVM methods have been widely investigated [11]- [23], such as least squares twin SVM (LSTSVM) [11], twin bounded SVM (TBSVM) [12], twin parametric-margin SVM (TPMSVM) [13], coordinate descent margin based twin SVM (CDMTSVM) [14], robust twin SVM (RTSVM) [15], nonparallel hyperplane SVM (NHSVM) [16], maximum margin of twin spheres support vector machine (MMTSSVM) [17], novel twin SVM (NTSVM) [18], wavelet twin SVM (WTWSVM) [19], angle-based twin SVM (ATSVM) [20], sparse pinball TSVM (SPTWSVM) [21], Pin-GTSVM [22] and improved universum TSVM (IUTSVM) [23]. In order to deal with complex XOR problem, inspired by multi-weight vector projection SVM (MVSVM) [24], Chen et al [25] proposed the projection twin SVM (PTSVM), which seeks a projection direction rather than a hyperplane for each class.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

et al. 2019

View full text Add to dashboard Cite

Twin support vector machine (TWSVM) is a new machine learning method, as opposed to solving a single quadratic programming problem in support vector machine (SVM), which generates two nonparallel hyperplanes by solving two smaller size quadratic programming problems. However, the TWSVM obtains the final classifier by giving the same importance to all training samples which may be important for classification performance. In order to address this problem, in this paper, we propose a novel entropy-based fuzzy twin bounded support vector machine (EFTBSVM) for binary classification problems. By considering the fuzzy membership value for each sample and assigning it based on the entropy value, the samples with higher class certainty are assigned to relatively larger fuzzy membership. In addition, the proposed EFTBSVM not only maintains the superior characteristics of the TWSVM but also exploits the structural risk minimization principle by introducing a regularization term. The experimental results achieved on synthetic datasets and benchmark datasets illustrate the effectiveness of the proposed method.

show abstract

Wavelet twin support vector machines based on glowworm swarm optimization

Cited by 60 publications

References 41 publications

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

Research on fingerprint classification based on twin support vector machine

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

Contact Info

Product

Resources

About