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Most problems in Machine Learning cater to classification and the objects ofuniverse are classified to a relevant class. Ranking of classified objects ofuniverse per decision class is a challenging problem. We in this paper proposea novel Rough Set based membership called Rank Measure to solve to thisproblem. It shall be utilized for ranking the elements to a particular class.It differs from Pawlak Rough Set based membership function which gives anequivalent characterization of the Rough Set based approximations. It becomesparamount to look beyond the traditional approach of computing membershipswhile handling inconsistent, erroneous and missing data that is typicallypresent in real world problems. This led us to propose the aggregate RankMeasure. The contribution of the paper is three fold. Firstly, it proposes aRough Set based measure to be utilized for numerical characterization of withinclass ranking of objects. Secondly, it proposes and establish the properties ofRank Measure and aggregate Rank Measure based membership. Thirdly, we apply theconcept of membership and aggregate ranking to the problem of supervised MultiDocument Summarization wherein first the important class of sentences aredetermined using various supervised learning techniques and are post processedusing the proposed ranking measure. The results proved to have significantimprovement in accuracy.

Rough Set based Aggregate Rank Measure & its Application to Supervised Multi Document Summarization