Feature choice may be a very necessary part for datamining, machinery learning and pattern recognition. Distance plays a very important role in Support Vector Machines (SVM) theory. Relief-F algorithm solves feature redundancy well but does not guarantee the maximum distance. To overcome this drawback, a feature subset selection algorithm is proposed that takes SVM average distance as estimation rule and sequential forward selection as search strategy. Using public knowledge set acquired from UCI, this algorithm is compared with the Relief-F. The results show that the recognition rate is over Relief-F with smaller selected options under computation quantity tolerant conditions

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Text Mining Based on Tax Comments as Big Data Analysis Using SVM and Feature Selection - 2018ABSTRACT:The tax provides an important role for the contributions of the economy and development of a rustic. The improvements
PROJECT TITLE :Distributed Feature Selection for Efficient Economic Big Data Analysis - 2018ABSTRACT:With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although
PROJECT TITLE :Automatic Feature Selection Technique for Next Generation Self-Organizing Networks - 2018ABSTRACT:Despite self-organizing networks (SONs) pursue the automation of management tasks in current cellular networks, the
PROJECT TITLE :Large-Scale Kernel-Based Feature Extraction via Low-Rank Subspace Tracking on a Budget - 2018ABSTRACT:Kernel-primarily based ways get pleasure from powerful generalization capabilities in learning a selection of
PROJECT TITLE :Feature Map Quality Score Estimation Through Regression - 2018ABSTRACT:Understanding the visual quality of a feature map plays a important role in many active vision applications. Previous works mostly rely on object-level

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry