Corpus ID: 236428734

Novel Span Measure, Spanning Sets and Applications

@article{Yadav2021NovelSM,
  title={Novel Span Measure, Spanning Sets and Applications},
  author={Nidhika Yadav},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12178}
}
Rough Set based Spanning Sets were recently proposed to deal with uncertainties arising in the problem in domain of natural language processing problems. This paper presents a novel span measure using upper approximations. The key contribution of this paper is to propose another uncertainty measure of span and spanning sets. Firstly, this paper proposes a new definition of computing span which use upper approximation instead of boundary regions. This is useful in situations where computing… 

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References

SHOWING 1-10 OF 23 REFERENCES
Rough Sets Based Approach to Reduct Approximation: RSBARA
TLDR
An algorithm is proposed which uses rough set theory to approximate the reduct and reduces the required computational effort to O[N2timesM], which compares with a particle swarm approach and other deterministic rough set reduction algorithms.
Data mining based on rough sets
The chapter is focused on the data mining aspect of the applications of rough set theory. Consequently, the theoretical part is minimized to emphasize the practical application side of the rough set
Neighborhood rough set based heterogeneous feature subset selection
TLDR
A neighborhood rough set model is introduced to deal with the problem of heterogeneous feature subset selection and Experimental results show that the neighborhood model based method is more flexible to deals with heterogeneous data.
Rough set-aided keyword reduction for text categorization
TLDR
This article investigates the applicability of RS theory to the IF/IR application domain and compares this applicability with respect to various existing TC techniques, and investigates the ability of the approach to generalize, given a minimum of training data.
Applying Rough Set Concepts to Clustering
TLDR
This chapter describes how a core concept of rough sets, the lower and upper approximation of a set, can be used in clustering and is shown to be useful for representing groups of highway sections, web users, and supermarket customers.
Fuzzy-rough attribute reduction with application to web categorization
TLDR
Experimental results show that fuzzy–rough reduction is more powerful than the conventional rough set-based approach, and classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method.
Fuzzy-Rough Sets Assisted Attribute Selection
TLDR
This paper investigates a novel approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses problems and retains dataset semantics and is applied to two challenging domains where a feature reducing step is important; namely, web content classification and complex systems monitoring.
Rough sets and information retrieval
TLDR
The theory of rough sets, which allows us to classify objects into sets of equivalent members based on their attributes, is introduced and compared to the Boolean, vector and fuzzy models of information retrieval.
Soft rough fuzzy sets and soft fuzzy rough sets
TLDR
A new soft rough set model is proposed and its properties are derived and a more general model called soft fuzzy rough set is established.
Text summarization using rough sets
  • Nouman Azam, Afzaal Ahmad
  • Computer Science
    2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)
  • 2016
TLDR
This work defined and examined three types of matrices based on an information table, namely, discernibility matrix, indiscernibility matrix and equal to one matrix, which represents a certain type of relationship between the objects of an Information table.
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