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Volume & Issue no: Volume 3, Issue 10, October 2014


Author Name:
Ankita Deshmukh and Prof. Dr. S.S. Sane
ABSTRACT Feature selection is an important concept in data mining. Batch learning is the mostly used learning algorithm in feature selection. Unlike Batch learning, online learning proves to be the most promising, efficient and scalable machine learning algorithm. Most existing studies of online learning require accessing all the features of training data. But, accessing all attributes becomes a problem when we deal with high dimensional data. To avoid this limitation, we investigate an online learner which will maintain a classifier having small and fixed number of attributes. The key challenge of online feature selection is how to make accurate prediction for an instance using a small number of active features. This is in contrast to the classical setup of online learning where all the features can be used for prediction. We aim to develop novel OFS approaches which are compared with previous classification algorithms and to analyse its performance for real-world datasets with full and partial inputs. Keywords: Classification, Feature Selection, High Dimensional Data, Online Learning
Cite this article:
Ankita Deshmukh and Prof. Dr. S.S. Sane , " REVIEW ON ONLINE FEATURE SELECTION" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 3, Issue 10, October 2014 , pp. 190-193 , ISSN 2319 - 4847.
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