Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm–support vector machines: HGASVM

Avcı, Engin

doi:10.1016/j.eswa.2007.11.014

Cited by 87 publications

(31 citation statements)

References 40 publications

(60 reference statements)

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“…With the maximization of the margin of this hyperplane, the division among the levels is maximized. In support vector machine, the set of points can be divided by linear and non-linear methods [30]. Considering the assumption that the sets are dividable linearly, hyperplanes with maximum margin are obtained so that the sets can be divided.…”

Section: Support Vector Machinementioning

confidence: 99%

Data Mining and Knowledge Extraction in the Risk Based Insurance Audit: A Case Study (City of Tehran)

Amirinia¹,

Kazemi²,

Darvish³

2015

International Journal of Advanced Research in Computer and Comm

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Nowadays, data mining techniques are widely used in various industries. The Social Security Insurance Audit, with more than two million audit cases nationwide and limited manpower, cannot respond to all cases. The present research, using data mining techniques, aims to propose an efficient method for the detection of high-risk companies and fraud in the insurance industry. Materials and Methods: After randomly collecting 2000 records from the Social Security Audit database in 2014 and extracting various features for each company, a pre-processing of the data was conducted. The data was then divided into the two categories of Training Set and Test Set and given to the following four algorithms: Neural Network, Decision Tree, Bayesian and Support Vector Machine. Ultimately the accuracy of each algorithm was measured by the confusion matrix. Findings: By comparing the four algorithms mentioned above, it can be seen that all of these algorithms can provide a proper level of accuracy, with the neural network algorithm showing 90.6% accuracy, was found to be the best algorithm for the identification of high-risk companies. The findings of this study enable the Social Security Audit to develop software which can identify companies with high insurance risk in order to achieve higher levels of efficiency in the face of current limitations in manpower.

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Section: Support Vector Machinementioning

confidence: 99%

Data Mining and Knowledge Extraction in the Risk Based Insurance Audit: A Case Study (City of Tehran)

Amirinia¹,

Kazemi²,

Darvish³

2015

International Journal of Advanced Research in Computer and Comm

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“…In the basic SVM approach with extraction features [21][22][23][24][25], the classifier separates the input feature vectors into two classes based on the maximal distance algorithm using the most powerful classifying functions, which defines the judgment boundary (two-dimensional space) or hyperplane (multidimensional space). The mathematical expression for the two classes of linear SVM classifiers can be defined as below: Figure 4.…”

Section: Support Vector Machine For Modulation Format Classificationmentioning

confidence: 99%

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

Wei

Huang

et al. 2016

Entropy

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Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE) and multiclass classification of support vector machine (SVM), we propose a method for recognition of multiple optical modulation formats and various data rates. We firstly present the algorithm of multidimensional ADTPE, which is extracted from asynchronous delay sampling pairs of modulated optical signal. Then, a multiclass SVM is utilized for fast and accurate classification of several widely-used optical modulation formats. In addition, a simple real-time recognition scheme is designed to reduce the computation time. Compared to the existing method based on asynchronous delay-tap plot (ADTP), the theoretical analysis and simulation results show that our recognition method can effectively enhance the tolerance of transmission impairments, obtaining relatively high accuracy. Finally, it is further demonstrated that the proposed method can be integrated in an optical transport network (OTN) with flexible expansion. Through simply adding the corresponding sub-SVM module in the digital signal processer (DSP), arbitrary new modulation formats can be recognized with high recognition accuracy in a short response time.

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“…In the last few years, ANN is the most preferred method [4] due to its ability to deal with non-linear factors, and the accuracy of continuous function mapping can be achieved by a three layer neural network [1]. However, ANN requires a lot of training sample data and the selected initial weights can get the local optimal easily [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of Building Load Forecasting Using Hybrid of GLSSVM – ABC Model

et al. 2016

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Abstract. In conducting load forecasting, the accuracy of forecasting is an important aspect in planning and managing electricity. Thus, a new hybrid model is presented in this paper, which combines the Group Method of Data Handling, Least Square Support Vector Machine and Artificial Bee Colony (GLSSVM-ABC) for building load forecasting. Its performance accuracy has been compared with other methods by using the Mean Absolute Percentage Error (MAPE) and Root Means Square Error (RMSE). It was found that the proposed method has resulted in better performance accuracy in terms of both MAPE and RMSE. The MAPE analysis showed an increase in performance accuracy of more than 7 percent when compared to other methods. The RMSE analysis showed an increase in performance accuracy of more than 5 percent when compared to other methods. The results in this study showed that the proposed method is proven to be effective and has great potential for accurate building load forecasting.

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Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm–support vector machines: HGASVM

Cited by 87 publications

References 40 publications

Data Mining and Knowledge Extraction in the Risk Based Insurance Audit: A Case Study (City of Tehran)

Data Mining and Knowledge Extraction in the Risk Based Insurance Audit: A Case Study (City of Tehran)

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

Enhancing the Performance of Building Load Forecasting Using Hybrid of GLSSVM – ABC Model

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