Most problems in Machine Learning cater to classification and the objects of
universe are classified to a relevant class. Ranking of classified objects of
universe per decision class is a challenging problem. We in this paper propose
a novel Rough Set based membership called Rank Measure to solve to this
problem. It shall be utilized for ranking the elements to a particular class.
It differs from Pawlak Rough Set based membership function which gives an
equivalent characterization of the Rough Set based approximations. It becomes
paramount to look beyond the traditional approach of computing memberships
while handling inconsistent, erroneous and missing data that is typically
present in real world problems. This led us to propose the aggregate Rank
Measure. The contribution of the paper is three fold. Firstly, it proposes a
Rough Set based measure to be utilized for numerical characterization of within
class ranking of objects. Secondly, it proposes and establish the properties of
Rank Measure and aggregate Rank Measure based membership. Thirdly, we apply the
concept of membership and aggregate ranking to the problem of supervised Multi
Document Summarization wherein first the important class of sentences are
determined using various supervised learning techniques and are post processed
using the proposed ranking measure. The results proved to have significant
improvement in accuracy.

本文提出了一种新的基于粗糙集的成员资格称为排名度量，用于排名特定类别的元素，特别是在处理存在于现实世界问题中的不一致，错误和缺失数据时。然后，我们将成员资格和排名的概念应用于有监督的多文档摘要问题，其中首先使用各种有监督的学习技术确定重要句子类，然后使用所提出的排名测量进行后处理。结果在准确性方面有显着提高。