Abstract
In high dimensional data, one of the challenges is to tackle high dimensional feature and label space. Currently, research is going on Extreme Multi-Label Classification, which is a supervised machine learning algorithm focusing on high dimensional data. “Extreme Multi-Label Classification” is the extension of Multi-Label Classification with high dimensional label space. One of the objectives is to extract important labels from high dimensional label space where traditional Multi-Label Classification approaches are failed. It is used in many classification applications such as Wikipedia Categorization, Product Recommendations, Tagging Applications, Search Query Recommendations, Word Recommendations etc. This paper covers challenges and approaches to handling the high dimensional Classification problem followed by potential directions for the improvements of the classifier.
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