Performance evaluation of support vector machine classification approaches in data mining

Chidambaram, S.; Srinivasagan, K. G.

doi:10.1007/s10586-018-2036-z

Cited by 25 publications

(13 citation statements)

References 15 publications

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“…The data of a product supplier are selected, processed after normalization, and input into the Bayesian network model [ 32 ], average support vector machine (ASVM) model [ 33 ], BP neural network model [ 34 ], PSO-BP neural network, RBF neural network model [ 35 ] and the designed and trained BP-GA model to test the evaluation results of the proposed model on enterprise performance and prove the effectiveness and feasibility of its application. The simulation test is based on factor analysis.…”

Section: Resultsmentioning

confidence: 99%

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

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The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. First, the evaluation indicators of enterprise performance are selected from both financial and non-financial aspects. An enterprise performance appraisal model is established based on the neural networks and optimized by the factor analysis method and Genetic Algorithm (GA). The principal factors affecting enterprise performance are analyzed. Then the M&A parties’ performances during the M&A commitment period under the earnings compensation mechanism are analyzed quantitatively. Corresponding hypotheses and evaluation indicators are established. Mean test results and regression analyses demonstrate that the hypotheses proposed are valid under particular circumstances. Introducing the earnings compensation mechanism during the M&A process can improve the enterprise performance effectively so that the earnings forecasted in the commitment period are significantly higher than the historical profitability. Hence, the earnings compensation mechanism plays a positive role in guiding enterprise performance. Comparison with models proposed in previous research reveals that the output error ratio of the designed corporate performance evaluation model is 1.16%, which can effectively evaluate corporate performance. The above results provide a reference for studying the impact of the earnings compensation mechanism on enterprise performance during the M&A process.

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Section: Resultsmentioning

confidence: 99%

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

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“…This kernel is the basic kernel that is most often used by SVM because with this kernel, SVM divides data linearly. The linear kernel formula is presented in Equation (2) [24].…”

Section: Linear Kernelmentioning

confidence: 99%

“…The polynomial kernel is a kernel that is suitable for problems where training data is normalized [24]. From Equation (3), σ is the parameter that must be settled.…”

Section: Polynomial Kernelmentioning

confidence: 99%

Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

et al. 2020

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Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

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“…Based on the previous research can be concluded that the Support Vector Machine method and the addition of Semantic Similarity to the testing process can enhance the accuracy [11]. Support Vector Machine learning method is one of the methods that functions to analyze data, which is used to classify and analyze regression, the use of Support Vector Machine can produce better accuracy values that can be applied to the case of data classification [14]. This case study uses N-gram as feature extraction with TF-IDF weighting, there are several features on N-gram, one of them is the Unigram feature which gives better results when compared to Bigram [9].…”

Section: Introductionmentioning

confidence: 99%

Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity

salsabila

Sibaroni

2021

RESTI

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Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the preprocessing stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect.

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Performance evaluation of support vector machine classification approaches in data mining

Cited by 25 publications

References 15 publications

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity

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