Underground target detection technology has been widely used in urban construction and resource exploration. With the development of industrial modernization, the demand for underground target detection is becoming mo...
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Underground target detection technology has been widely used in urban construction and resource exploration. With the development of industrial modernization, the demand for underground target detection is becoming more specific, such as the material and shape of underground targets. Therefore, it is necessary to classify the properties of underground targets. In this paper, sensitivity analysis was performed on the spheroid model and the approximate forward model at first, and the influence of the target properties on the model output is obtained. Secondly, we utilized the fitting algorithm to obtain the model parameters of the simulation data (model response of targets with varying shapes and materials), and analyzed the influence of the fitting algorithm on the classification results at different SNR. Finally, eight machine learning algorithms: support vector machine (SVM), neural network (NN), quadratic discriminant analysis (QDA), Gaussian process (GP), decision tree (DT), random forest (RF) and AdaBoost were used in this study to compare the obtained results. From the above analysis, we found that the shape (radius) have a greater influence on the model than the material (permeability) in the spheroid model. According to the approximate forward model, we found that it is not feasible to classify targets when the orientation is unknown. The influence of the fitting algorithm on the classification performances is related to the noise level. The obtained results using neural network demonstrated that the proposed method outperformed in material-based classification and shape-based classification. In the material-based classification, the classifier generally has a weaker ability to distinguish between permeable materials.
In this paper, we make an experimental study to compare the performances of different data mining classificationalgorithms for predicting osteoporosis in Tunisian postmenopausal women. This study aims to identify the...
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ISBN:
(纸本)9783319439495;9783319439488
In this paper, we make an experimental study to compare the performances of different data mining classificationalgorithms for predicting osteoporosis in Tunisian postmenopausal women. This study aims to identify the best algorithms with the optimum classification parameters values and to determine the most important risk factors that have a significant impact on the osteoporosis occurrence. The obtained results show that Support Vector Machine (SVM) classifier and Artificial Neural Network (ANN) classifier give the best classification performances when dealing with the three bone statuses (normal, osteopenia, osteoporosis). On the other hand, the decision tree classifier C4.5 enables to extract the most important risk factors for osteoporosis occurrence. The selected risk factors are validated by biologists.
Performance assessment of a learning method related to its prediction ability on independent data is extremely important in supervised classification. This process provides the information to evaluate the quality of a...
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Performance assessment of a learning method related to its prediction ability on independent data is extremely important in supervised classification. This process provides the information to evaluate the quality of a classification model and to choose the most appropriate technique to solve the specific supervised classification problem at hand. This paper aims to review the most important aspects of the evaluation process of supervised classificationalgorithms. Thus the overall evaluation process is put in perspective to lead the reader to a deep understanding of it. Additionally, different recommendations about their use and limitations as well as a critical view of the reviewed methods are presented according to the specific characteristics of the supervised classification problem scenario.
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