Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...

Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...

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...

Feature selection is an important issue for any learning algorithm, since reduced feature sets lead to an improvement in learning time, reduced model complexity and, in many cases, a reduced risk of overfitting. When performing feature selection for RAM-based learning algorithms, we typically assume that the cost of accessing each feature is uniform. In multirelational data mining, especially w...

Feature selection is an important issue for any learning algorithm, since reduced feature sets lead to an improvement in learning time, reduced model complexity and, in many cases, a reduced risk of overfitting. When performing feature selection for RAM-based learning algorithms, we typically assume that the cost of accessing each feature is uniform. In multirelational data mining, especially w...

Introduction: To develop different radiomic models based on radiomic features and machine learning methods to predict early intensity modulated radiation therapy (IMRT) response.   Materials and Methods: Thirty prostate patients were included. All patients underwent pre ad post-IMRT T2 weighted and apparent diffusing coefficient (ADC) magnetic resonance imagi...

Software cost estimation is one of the most crucial activities in software development process. In the past decades, many methods have been proposed for cost estimation. Case Based Reasoning (CBR) is one of these techniques. Feature selection is an important preprocessing stage of case based reasoning. Most existing feature selection methods of case-based reasoning are ‘wrappers’ which can usua...

Feature selection is an essential process for machine learning tasks since it improves generalization capabilities, and reduces run-time and amodel’s complexity. Inmany applications, the cost of collecting the features must be taken into account. To cope with the cost problem, we developed a new cost-sensitive fitness function based on histogram comparison. This function is integrated with a ge...