论文标题
基于粗糙集的汇总等级度量及其在监督多文档摘要中的应用
Rough Set based Aggregate Rank Measure & its Application to Supervised Multi Document Summarization
论文作者
论文摘要
机器学习中的大多数问题都符合分类和宇宙的对象分类为相关类。每个决策类别的宇宙分类对象的排名是一个具有挑战性的问题。我们在本文中提出了一种基于新颖的基集成员资格，称为“等级度量”，以解决此问题。它应用于将元素对特定类别进行排名。它不同于基于Pawlak粗糙集的成员资格功能，该功能给出了基于粗糙集的近似值的等效表征。在处理不一致，错误和缺失的数据的同时，这些方法通常存在于现实世界中的问题中，这是至关重要的。这导致我们提出了总等级度量。纸的贡献是三倍。首先，它提出了一种基于粗糙集的度量，用于用于对象的类排名的数值表征。其次，它提出并建立了基于等级措施的属性和基于总等级措施的成员资格。第三，我们将成员资格和汇总排名的概念应用于监督的多文档摘要问题，其中首先使用各种监督学习技术确定重要的句子类别，并使用拟议的排名措施进行后处理。结果证明，准确性有显着提高。
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.
