A Framework To Integrate Feature Selection Algorithm For Classification Of High Dimensional Data
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 5
Abstract
The explosive usage of social media produces huge quality of unlabeled and high-dimensional data. The data characteristic with this choice has been tested to be powerful in handling excessive-dimensional facts for effective learning and data mining. In this high-dimensional unsupervised function choice stays a tough task due to the absence of label facts based on which feature relevance is frequently assessed. The specific characteristic of social media statistics further complicate the difficult hassle of unsupervised characteristic selection which makes invalid and identically allotted assumption. In this context bringing approximately new demanding situations to unsupervised characteristic selection algorithmsis a big task. In this paper, we proposed a multiple trouble of function choice for social media records in an unmonitored scenario.Next, analyze the variations among social media data and traditional attribute-fee statistics which looks into the family members extracted from linked statistics to be exploited for selecting applicable functions. Finally, advocate a novel unsupervised feature choice framework, WSLA(Web Server Log Analyzer), for related social media information. Systematically style and implement the general experiments to assess the planned framework on info sets from real-global social media internet sites.The empirical study reveals the learing space of unsupervised feature selection is more powerful and can be extended to different without labeled data with additional information.
Authors and Affiliations
Durga S, Lokeshkumar R
Keywords
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