Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress

Lee, Ming-Chang; Chang, To

doi:10.5121/ijaia.2010.1303

Cited by 54 publications

(36 citation statements)

References 22 publications

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“…Nor was a significant difference found by Lee and To (2010) in performance between neural networks and the SVM approach. They divided their sample of 45 companies listed on the Taiwan stock exchange into a training and a test sample with 20 and 25 items, respectively.…”

Section: Dong Et Almentioning

confidence: 93%

Application of support vector machines on the basis of the first Hungarian bankruptcy model

Virág

Nyitrai²

2013

Society and Economy

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In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks. JEL classification codes: C33, C45, C51, C52, C53, G33

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Section: Dong Et Almentioning

confidence: 93%

Application of support vector machines on the basis of the first Hungarian bankruptcy model

Virág

Nyitrai²

2013

Society and Economy

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“…A well-trained ANN should consist of the optimal numbers of hidden layer neurons, hidden layer(s), and weight values, which sufficiently avoid the risk of either under-or overfitting. In practical applications, there is a large number of neural networks with modified algorithms, such as ELM [43][44][45], backpropagation neural network (BPNN) [46][47][48], and general regression neural network (GRNN) [49][50][51]. Though there are various network models, the basic principles for model training are similar.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Liu

et al. 2017

International Journal of Photoenergy

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Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a highperformance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

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“…Evaluation of SVM and BPN is done in [3] and [4] where BPN outperforms SVM. In [3], BPN gives 10% more accuracy than SVM and is determined as the best classifier for predicting proteins sequence based on their compositions, whereas in [5] and [6], SVM outperforms BPN. The results depend on the dataset and the type of classification problem.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Banknote Authentication System using Machine Learning Techniques

Shahani¹,

Jagiasi²,

Priya³

2018

IJCA

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Banknotes are one of the most important assets of a country. Some miscreants introduce fake notes which bear a resemblance to original note to create discrepancies of the money in the financial market. It is difficult for humans to tell true and fake banknotes apart especially because they have a lot of similar features. Fake notes are created with precision, hence there is need for an efficient algorithm which accurately predicts whether a banknote is genuine or not. This paper proposes machine learning techniques to evaluate authentication of banknotes. Supervised learning algorithms such as Back propagation Neural Network (BPN) and Support Vector Machine (SVM) are used for differentiating genuine banknotes from fake ones. The study also shows the comparison of these algorithms in classification of banknotes.

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Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress

Cited by 54 publications

References 22 publications

Application of support vector machines on the basis of the first Hungarian bankruptcy model

Application of support vector machines on the basis of the first Hungarian bankruptcy model

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Analysis of Banknote Authentication System using Machine Learning Techniques

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