scholarly journals Classification of Incidental Carcinoma of the Prostate Using Learning Vector Quantization and Support Vector Machines

2004 ◽  
Vol 26 (1-2) ◽  
pp. 45-55
Author(s):  
Torsten Mattfeldt ◽  
Danilo Trijic ◽  
Hans‐Werner Gottfried ◽  
Hans A. Kestler
Keyword(s):  
Support Vector Machines ◽  
Gleason Score ◽  
Vector Quantization ◽  
Volume Fraction ◽  
Learning Vector Quantization ◽  
Support Vector ◽  
Stepwise Logistic Regression ◽  
P53 Overexpression ◽  
Vector Machines ◽  
Input Variables

The subclassification of incidental prostatic carcinoma into the categories T1a and T1b is of major prognostic and therapeutic relevance. In this paper an attempt was made to find out which properties mainly predispose to these two tumor categories, and whether it is possible to predict the category from a battery of clinical and histopathological variables using newer methods of multivariate data analysis. The incidental prostatic carcinomas of the decade 1990–99 diagnosed at our department were reexamined. Besides acquisition of routine clinical and pathological data, the tumours were scored by immunohistochemistry for proliferative activity and p53‐overexpression. Tumour vascularization (angiogenesis) and epithelial texture were investigated by quantitative stereology. Learning vector quantization (LVQ) and support vector machines (SVM) were used for the purpose of prediction of tumour category from a set of 10 input variables (age, Gleason score, preoperative PSA value, immunohistochemical scores for proliferation and p53‐overexpression, 3 stereological parameters of angiogenesis, 2 stereological parameters of epithelial texture). In a stepwise logistic regression analysis with the tumour categories T1a and T1b as dependent variables, only the Gleason score and the volume fraction of epithelial cells proved to be significant as independent predictor variables of the tumour category. Using LVQ and SVM with the information from all 10 input variables, more than 80 of the cases could be correctly predicted as T1a or T1b category with specificity, sensitivity, negative and positive predictive value from 74–92%. Using only the two significant input variables Gleason score and epithelial volume fraction, the accuracy of prediction was not worse. Thus, descriptive and quantitative texture parameters of tumour cells are of major importance for the extent of propagation in the prostate gland in incidental prostatic adenocarcinomas. Classical statistical tools and neuronal approaches led to consistent conclusions.

scholarly journals A support vector machine method for bid/no bid decision making

2017 ◽  
Vol 23 (5) ◽  
pp. 641-649 ◽  
Author(s):  
Rifat SONMEZ ◽  
Burak SÖZGEN
Keyword(s):  
Decision Making ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Support Vector Machine Classifier ◽  
Oil And Gas ◽  
Support Vector ◽  
Machine Method ◽  
Generalization Performance ◽  
Vector Machines ◽  
Input Variables

The bid/no bid decision is an important and complex process, and is impacted by numerous variables that are related to the contractor, project, client, competitors, tender and market conditions. Despite the complexity of bid decision making process, in the construction industry the majority of bid/no bid decisions is made informally based on experience, judgment, and perception. In this paper, a procedure based on support vector machines and backward elimination regression is presented for improving the existing bid decision making methods. The method takes advan­tage of the strong generalization properties of support vector machines and attempts to further enhance generalization performance by eliminating insignificant input variables. The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. The results show that the support vector machine classifier outperforms existing methods significantly, and the proposed procedure provides a powerful tool for bid/no bid decision making. The results also reveal that elimination of the insignificant input variables improves generalization performance of the sup­port vector machines.

scholarly journals Assessment of Three Learning Machines for Long-Term Prediction of Wind Energy in Palestine

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tamer Khatib ◽  
Reziq Deria ◽  
Asma Isead
Keyword(s):  
Neural Network ◽  
Support Vector Machines ◽  
Wind Energy ◽  
Random Forests ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Energy Data ◽  
Vector Machines ◽  
Input Variables

In this research, an approach for predicting wind energy in the long term has been developed. The aim of this prediction is to generate wind energy profiles for four cities in Palestine based on wind energy profile of another fifth city. Thus, wind energy data for four cities, namely, Nablus city, are used to develop the model; meanwhile, wind energy data for Hebron, Jenin, Ramallah, and Jericho cities are predicted based on that. Three machine learning algorithms are used in this research, namely, Cascade-forward neural network, random forests, and support vector machines. The developed models have two input variables which are daily average cubic wind speed and the standard deviation, while the target is daily wind energy. The R-squared values for the developed Cascade-forward neural network, random forests, and support vector machines models are found to be 0.9996, 0.9901, and 0.9991, respectively. Meanwhile, RMSE values for the developed models are found to be 41.1659 kWh, 68.4101 kWh, and 205.10 kWh, respectively.

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