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...

Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...

Even though a great attention has been given on the cervical cancer diagnosis, it is a tuff task to observe the pap smear slide through microscope. Image Processing and Machine learning techniques helps the pathologist to take proper decision. In this paper, we presented the diagnosis method using cervical cell image which is obtained by Pap smear test. Image segmentation performed by multi-thr...

The application of feature selection techniques greatly reduces the computational cost of classifying highdimensional data. Feature selection algorithms of varying performance and computational complexities have been studied previously. This paper compares the performance of classical sequential methods, a floating search method, and the “globally optimal” branch and bound algorithm when applie...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

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...

Objective: Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction M...

Feature selection is a critical procedure in many pattern recognition applications. There are two distinct mechanisms for feature selection namely the wrapper methods and the filter methods. The filter methods are generally considered inferior to wrapper methods, however wrapper methods are computationally more demanding than filter methods. A novel filter feature selection method based on mutu...

This paper developed a diagnosis model based on Support Vector Machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our hybrid feature selection method, named IFSFFS (Improved F -score and Sequential Forward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In our...