Robust Feature Selection Technique Using Rank Aggregation
نویسندگان
چکیده
منابع مشابه
Fusion of SFS-SVM feature selection methods using robust rank aggregation for optimal feature subset selection for mammogram classification
Feature selection and classification plays an important role in the design and development of a computer aided detection and diagnostics (CAD) tool for breast cancer detection from mammograms. In literature, the various feature selection methods exists such as filter based, wrapper based, and hybrid methods whose aim is to select the most relevant and minimum redundant features from the extract...
متن کاملRank Aggregation for Filter Feature Selection in Credit Scoring
The credit industry is a fast growing field, credit institutions collect data about credit customer and use them to build credit model. The collected information may be full of unwanted and redundant features which may speed down the learning process, so, effective feature selection methods are needed for credit dataset. In general, Filter feature selection methods outperform other feature sele...
متن کاملRobust Feature Selection Using Ensemble Feature Selection Techniques
Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, where multiple feature selection methods are combined to yield more robust results....
متن کاملFeature Selection Method Using Preferences Aggregation
The feature selection allows to choose P features among M (P<M) and thus to reduce the representation space of data. This process is increasingly useful because of the databases size increase. Therefore we propose a method based on preferences aggregation. It is an hybrid method between filter and wrapper approaches.
متن کاملCorrelation based Feature Selection using Rank aggregation for an Improved Prediction of Potentially Preventable Events
This paper presents a methodology for developing a novel feature selection model that will help in a more accurate and robust prediction of patients with the risk of Potentially Preventable Events (PPEs). PPEs are admissions, readmissions, complications and emergency department visits that could have been avoided if the patient had been given the appropriate interventions. Various clinical fact...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Artificial Intelligence
سال: 2014
ISSN: 0883-9514,1087-6545
DOI: 10.1080/08839514.2014.883903