Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

Lin, Kuan‐Cheng; Chen, Sih-Yang; Hung, Jason C.

doi:10.1155/2015/604108

Cited by 15 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fitness function is many times expressed as a classification accuracy. Many researchers use SVMs as the classifier method to evaluate solutions (e.g., [33,38,[50][51][52]). Researchers in [46] proposed fitness function that is the average of the classification error rate obtained by three classifiers, SVM variations, υ-SVM, C-SVM and LS-SVM in the DA algorithm.…”

Section: Fitness Functionmentioning

confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

View full text Add to dashboard Cite

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

show abstract

Section: Fitness Functionmentioning

confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

View full text Add to dashboard Cite

show abstract

“…The optimization principle of the method of grid search can obtain the mean square error CVmse in this set of values by the method of K-CV (K fold crossvalidation) for a certain combination of the parameters (c,g). CVmse is shown as (14). At last, the set of values which made CVmse highest was determined as the optimal parameter.…”

Section: The Methods Of Grid Searchmentioning

confidence: 99%

“…Support Vector Machine (SVM) [26] has a complete theory system and better performance, and is widely used in many fields [4,6,17]. The two most important factors to influence the classification performance of SVM are [8,14]: (1) the choice of kernel function and the parameter of g. (2) the penalty parameter C. In addition, the original data set has also important influence on classification results. Thereby, data preprocessing, which includes de-noising and feature selection, is needed.…”

Section: Introductionmentioning

confidence: 99%

Relief Feature Selection and Parameter Optimization for Support Vector Machine based on Mixed Kernel Function

Zhang¹

2018

IJPE

View full text Add to dashboard Cite

In order to improve the classification performance of Support Vector Machine (SVM), Relief feature selection algorithm was used to obtain the most relevant feature subset and remove redundant features. The mixed kernel function, which combined the global kernel function with the local kernel function, was proposed to strengthen the learning ability and generalization performance of SVM. In addition, the parameter optimization of SVM, which combined Genetic Algorithm (GA) with grid search, was performed to reduce computation time and find optimal solutions. Finally, the methods presented in this paper were used in the Heart disease data set and the Breast cancer data set in the UCI. Compared with KNN and BP neural network, the classification result of SVM model with Relief algorithm and mixed kernel function significantly outperformed the other comparable classification model and the experimental results demonstrate the validity of the proposed model.

show abstract

“…However, in multimodal problems, the PSO has the disadvantage of trapping in a local optimum. Lin et al proposed a modified artificial fish swarm algorithm for choosing the hyperparameters of SVMs [34]. Artificial fish swarm algorithms have been verified effective in numerous studies, but they lack diversity.…”

Section: Introductionmentioning

confidence: 99%

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

2019

View full text Add to dashboard Cite

In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand.

show abstract

Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

Cited by 15 publications

References 21 publications

Swarm Intelligence Algorithms for Feature Selection: A Review

Swarm Intelligence Algorithms for Feature Selection: A Review

Relief Feature Selection and Parameter Optimization for Support Vector Machine based on Mixed Kernel Function

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

Contact Info

Product

Resources

About