objective(s): this study addresses feature selection for breast cancer diagnosis. the present process uses a wrapper approach using ga-based on feature selection and ps-classifier. the results of experiment show that the proposed model is comparable to the other models on wisconsin breast cancer datasets. materials and methods: to evaluate effectiveness of proposed feature selection method, we ...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...

The paper presents and compares the data mining techniques for selection of the diagnostic features in the problem of blood cell recognition in leukemia. Different techniques are compared, including the linear SVM ranking, correlation and statistical analysis of centers and variances of clusters corresponding to different classes. We have applied radial kernel SVM as the classifier. The results...

Often, the explanatory power of a learned model must be traded off against model performance. In the case of predicting runaway software projects, we show that the twin goals of high performance and good explanatory power are achievable after applying a variety of data mining techniques (discrimination, feature subset selection, rule covering algorithms). This result is a new high water mark in...

Evolutionary algorithms have been successfully used in different data mining problems. Given that the prototype selection problem could be seen as a combinatorial problem, evolutionary algorithms have been used to solve it with promising results. This chapter presents an evolutionary data mining application known as evolutionary prototype selection. Various approaches have been proposed in the ...