Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis

Ye, Fei

doi:10.1007/s11042-016-4233-1

Cited by 25 publications

(9 citation statements)

References 32 publications

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“…GA: This algorithm consists a series of genetic operations, such as selection and crossover, which are mutations, to generate a new generation of groups which are gradually evolved to be included or become close to the optimal solution [ 22 ]. In feature selection, first, the feature set to be optimized and C and γ in the SVM classifier are encoded into a chromosome.…”

Section: Methodsmentioning

confidence: 99%

Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy

Zhou

Zhang

Wang

et al. 2018

Sensors

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With the advent of high spatial resolution remote sensing imagery, numerous image features can be utilized. Applying a reasonable feature selection approach is critical to effectively reduce feature redundancy and improve the efficiency and accuracy of classification. This paper proposes a novel feature selection approach, in which ReliefF, genetic algorithm, and support vector machine (RFGASVM) are integrated to extract buildings. We adopt the ReliefF algorithm to preliminary filter high-dimensional features in the feature database. After eliminating the sorted features, the feature subset and the C and γ parameters of support vector machine (SVM) are encoded into the chromosome of the genetic algorithm. A fitness function is constructed considering the sample identification accuracy, the number of selected features, and the feature cost. The proposed method was applied to high-resolution images obtained from different sensors, GF-2, BJ-2, and unmanned aerial vehicles (UAV). The confusion matrix, precision, recall and F1-score were applied to assess the accuracy. The results showed that the proposed method achieved feature reduction, and the overall accuracy (OA) was more than 85%, with Kappa coefficient values of 0.80, 0.83 and 0.85, respectively. The precision of each image was more than 85%. The time efficiency of the proposed method was two-fold greater than SVM with all the features. The RFGASVM method has the advantages of large feature reduction and high extraction performance and can be applied in feature selection.

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Section: Methodsmentioning

confidence: 99%

Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy

Zhou

Zhang

Wang

et al. 2018

Sensors

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“…SVMs are based manly on the idea of finding the best hyperplane that maximizes the margin (distance to nearest points) between the nearest +ve and -ve data points [13], the class boundary for linearly separable data, giving a greater chance of new data being classified correctly [14], assume the training data has the dataset data={yi, xi}; i=1,2, . .…”

Section: Support Vector Machinementioning

confidence: 99%

Simulated annealing for SVM parameters optimization in student’s performance prediction

2021

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High education is an important and critical part of education all over the world. In last year, the world has been turned increasingly to online education due to the outbreak of the Covid-19 pandemic; therefore, improving this education system became an urgent matter. Online learning systems are a primal environment for acquiring educational data which can be from different sources, especially academic institutions. These data can be mainly used to analyze and extract utilizable information to help in understanding university students’ performance and identifying factors that affect it. To extract some meaningful information from these large volumes of data, academic organizations must mine the data with high accuracy. In this work, three different real datasets were selected, pre-processed, cleaned, and filtered for applying support vector machine (SVM) with multilayer perceptron kernel (MLP kernel) and optimize its parameters using simulated annealing (SA) algorithm to improve the objective function value. While examining the search space, SA has the advantage of escaping from local minima since it offers the chance for accepting the worse neighbor as a solution in a controlled manner. The results show that the designed system can determine the best SVM parameters using SA and therefore presents better model evaluation.

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“…Numerous statistical and soft computing based classification approaches have been applied in the classification field. The major drawback of statistical methods [9,10] is that these methods are not efficient in case of nonlinear problems. Like statistical methods, soft computing methods also have some merits and demerits.…”

Section: Introductionmentioning

confidence: 99%

“…The drawbacks of soft computing approaches [11] are: training time of high dimensional datasets is very high, selecting an optimum kernel function is quite difficult and the final model depends on variable weights which makes it difficult to interpret. According to the literature survey, SVM [9,10] and Artificial Neural Network (ANN) have been applied by the researchers usually and extensively. In SVM, it is difficult to fine-tune the hyper-parameters like gamma and Cost-C.…”

Section: Introductionmentioning

confidence: 99%

Medical Data Classification Using Jaya Optimized ELM

Debata

Mohapatra

2020

Smart Innovation, Systems and Technologies

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The implication of machine learning approaches in healthcare issues makes the diagnosis and prognosis of cancer like deadly diseases easier and affordable. The sole aim of machine learning researchers is to design a robust classification model that can classify the medical data with high accuracy and less computational complexity. Here, Jaya optimized extreme learning machine (Jaya-ELM) is proposed for classifying medical data. To estimate the efficacy of the proposed model, two benchmark medical datasets, Lung Cancer and Breast Cancer, are collected from the UCI repository. The classification accuracy percentage is taken as a performance evaluation measure in this model. The result of this model is compared with the existing Neural Network (NN), Jaya optimized NN (Jaya-NN) and ELM models. The experimental result shows that the Jaya-ELM is significantly better than other models.

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Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis

Cited by 25 publications

References 32 publications

Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy

Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy

Simulated annealing for SVM parameters optimization in student’s performance prediction

Medical Data Classification Using Jaya Optimized ELM

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