Evolving support vector machines using fruit fly optimization for medical data classification

Shen, Liming; Chen, Huiling; Yu, Zhe; Kang, Wenchang; Bing-yu, Zhang; Li, Huaizhong; Yang, Bo; Liu, Dayou

doi:10.1016/j.knosys.2016.01.002

Cited by 505 publications

(182 citation statements)

References 32 publications

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“…For the parameter setting of traditional PSO, the inertia W is set to 1.0 and the acceleration coefficients C 1 and C 2 are set to 2.05 and 2.05, respectively [15, 28]. For the parameter setting of the TVPSO, the lower inertia weight W min and the upper inertia weight W max are set to 0.5 and 0.9, respectively.…”

Section: The Proposed Methodsologymentioning

confidence: 99%

“…Ding S et al [27] used FOA for parameter setting turning of TWSVM, and the experimental results showed that using FOA to optimize TWSVM can yield better classification performance than SVM. To solve the medical data classification problem, [28] used FOA to optimize SVM by determining an optimal parameter setting of the SVM model; compared with other well-known methods, the results of the constructed experiments of this method showed that the optimized SVM model with FOA is a powerful tool for medical data classification. For community detection methods, which are generally based on one evolutionary algorithm, a novel multi-swarm FOA was proposed by Liu Q et al [29], namely, CDMFOA, and the experimental results showed that this method can effectively solve the detection community structure.…”

Section: Introductionmentioning

confidence: 99%

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An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

Lou

Sun

2017

PLoS ONE

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This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

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Section: The Proposed Methodsologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

Lou

Sun

2017

PLoS ONE

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“…w, d boyutlu katsayı vektörünü, x verileri, y etiketleri, b ise bir offset değeri ifade eder. Doğrusal DVM, karesel optimizasyon ile amaç fonksiyonunu optimize ederek ayırma düzleminin bulunmasını sağlar [27]. DVM is istatistiksel öğrenme teorisinde yapısal bir öğrenme prosedürüdür [14].…”

Section: Destek Vektör Makineleriunclassified

The classification of breast cancer with Machine Learning Techniques

Kolay

Erdoğmuş

2016

2016 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

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ÖzetçeBu çalışmada UCI(University of California-Irvine) Machine Learning Laboratuvarından alınan göğüs kanseri dataları kullanılarak, makine öğrenmesi teknikleri ile göğüs kanserinin sınıflandırılmasına çalışılmıştır. Bu amaçla, göğüs kanseri dataları kullanılarak, K-means kümeleme uzaklık ölçümlerinin kümeleme başarısı Matlab©'da analiz edilmiştir. Daha sonra, hiçbir ön işleme yapmadan, Weka© Veri Madenciliği yazılımı kullanılarak, UCI'den alınan göğüs kanseri dataları, çeşitli makine öğrenme teknikleri ile WEKA'da sınıflandırılmıştır. Sonuç olarak kümeleme uzaklık ölçütlerinin başarıyı yaklaşık olarak %12 değiştirdiği ve sınıflandırma başarısının yöntemlere göre %45-%79 arasında değiştiği görülmüştür. AbstractIn this study, it is aimed to classify breast cancer data attained from UCI(University of California-Irvine), Machine Learning Laboratory with some Machine Learning Techniques. With this aim, clustering performance of some distance measures in Matlab© has been compared, using breast cancer data. Later without using any pre-processing, some of the machine learning techniques are used for the clustering breast cancer data, using WEKA data mining software©. As a result, it has been seen that distance measures effects the clustering performance nearly 12 percentage and the succes of the classification varies from %45 to %79, according to the methods.

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“…Since its introduction, SCA has successfully found its applications for many practical problems [13][14][15][16][17][18]. However, like other intelligent algorithms [19][20][21][22][23][24][25], the original SCA is easy to fall into the local optimum when solving the practical problems. In this study, we try to use the chaotic local search (CLS) strategy to enhance the local search capability of SCA, termed as CSCA.…”

Section: Introductionmentioning

confidence: 99%

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Lin

Chen

et al. 2019

Mathematical Problems in Engineering

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Under the background of "innovation and entrepreneurship," how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF-CSCA-SVM, for predicting college students' entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF-CSCA-SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students' decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta-heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.

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Evolving support vector machines using fruit fly optimization for medical data classification

Cited by 505 publications

References 32 publications

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

The classification of breast cancer with Machine Learning Techniques

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

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