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 ...

Feature selection is one of key issues related with data pre-processing of classification task in a data mining process. Although many efforts have been done to improve typical feature selection algorithms (FSAs), such as filter methods and wrapper methods, it is hard for just one FSA to manage its performances to various datasets. To above problems, we propose another way to support feature se...

This paper considers the problem of effective feature extraction acoustic signals from oil and gas pipelines under different working conditions. A pipeline leakage detection method is proposed based on multi-feature entropy fusion local linear embedding (LLE). First, seven kinds commonly used which can reflect characteristics signal better are extracted through experiments, including permutatio...

Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from...

Meteorological visibility is an important meteorological observation indicator to measure the weather transparency which for transport safety. It a challenging problem estimate visibilities accurately from image characteristics. This paper proposes transfer learning method estimation based on feature fusion. Different existing methods, proposed estimates data processing and features’ extraction...