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The county level of basic public services analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of basic public services data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of basic public services. The method...
Individual credit risk evaluation is an important and challenging data mining problem in financial analysis domain. This paper compares the effectiveness of four data mining algorithms - logistic regression (LR), decision tree (C4.5), support vector machine (SVM) and neural networks (NN) by applying them to two credit data sets. Experiment results show that the LR and SVM algorithms produced the best...
City innovative capability analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capability for country. According to the city innovative capability data which is large scale and imbalance, this paper presented a support vector machine model to predict city innovative capability. The method was compared with artificial neural network,...
Artificial neural network (ANN) shows good nonlinear mapping ability in many applications compared to traditional algorithms. In many applications, it is now widely used to extract knowledge from the train neural network. The fact that the model obtained with neural network is not understandable in terms of black box model is a brake to their use in this field. To enhance the explanation of ANN, a...
The paper compares the classification performance rate of eight models: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), case-base reasoning (CBR), and three decision trees (DTs). We build models and test their classification accuracy rates on a historical data set provided by a German financial institution. The data set contains...
To date, higher educational organizations are placed in a very high competitive environment. To remain competitive, one approach is to tackle the student and administration challenges through the analysis and presentation of data, or data mining. This study presents the results of applying data mining to enrollment data of Sebha University in Libya. The results can be used as a guideline or roadmap...
Corporate credit ratings are important financial indicators of investment risks. Traditional credit rating models employ classical econometrics methods with heteroscedasticity adjustments across various industries. In this paper, we propose using machine learning techniques in predicting corporate ratings and demonstrate, empirically, that multiclass machine learning algorithms outperform traditional...
Artificial neural networks (ANNs) are a powerful and widely used pattern recognition technique. However, they remain "black boxes" giving no explanation for the decisions they make. This paper presents a new algorithm for extracting a logistic model tree (LMT) from a neural network, which gives a symbolic representation of the knowledge hidden within the ANN. Landwehr's LMTs are based on...
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