The Application of Incomplete Information System of Rough Set Theory in Spam Filtering

2014 ◽  
Vol 11 (11) ◽  
pp. 3847-3856
Author(s):  
Yuanning Liu
Keyword(s):  
Information System ◽  
Incomplete Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Spam Filtering ◽  
Incomplete Information System

scholarly journals Rough Set Extension under Incomplete Information System with “?” Values

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ahmed Hamed Hussein
Keyword(s):  
Information System ◽  
Incomplete Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Extended Model ◽  
Approximation Accuracy ◽  
Incomplete Information System ◽  
Similarity Class ◽  
Indiscernible Objects

Classical rough set theory (RST) can't process incomplete information system (IIS) because it is based on an indiscernibility relation which is a kind of equivalent relation. In the literature a non-symmetric similarity relation based rough set model (NS-RSM) has been introduced as an extended model under IIS with ``?" values directly. Unfortunately, in this model objects in the same similarity class are not necessarily similar to each other and may belong to different target classes. In this paper, a new inequivalent relation called Maximal Limited Consistent block relation (MLC) is proposed. The proposed MLC relation improves the lower approximation accuracy by finding the maximal limited blocks of indiscernible objects in IIS with ``?" values. Maximal Limited Similarity rough set model (MLS) is introduced as an integration between our proposed relation (MLC) and NS-RSM. The resulted MLS model works efficiently under IIS with ``?" values. Finally, an illustrative example is given to validate MLS model. Furthermore, approximation accuracy comparisons have been conducted among NS-RSM and MLS. The results of this work demonstrate that the MLS model outperform NS-RSM.

scholarly journals Analysis of an Incomplete Information System Using the Rough Set Theory

2014 ◽  
pp. 193-203 ◽  
Author(s):  
C. I. Faustino Agreira ◽  
M. M. Travassos Valdez ◽  
C. M. Machado Ferreira ◽  
F. P. Maciel Barbosa
Keyword(s):  
Information System ◽  
Incomplete Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Incomplete Information System

THE ALGORITHM ON KNOWLEDGE REDUCTION IN INCOMPLETE INFORMATION SYSTEMS

2002 ◽  
Vol 10 (01) ◽  
pp. 95-103 ◽  
Author(s):  
JIYE LIANG ◽  
ZONGBEN XU
Keyword(s):  
Information System ◽  
Information Systems ◽  
Incomplete Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Np Hard ◽  
Knowledge Reduction ◽  
Np Hard Problem ◽  
Incomplete Information Systems

Rough set theory is emerging as a powerful tool for reasoning about data, knowledge reduction is one of the important topics in the research on rough set theory. It has been proven that finding the minimal reduct of an information system is a NP-hard problem, so is finding the minimal reduct of an incomplete information system. Main reason of causing NP-hard is combination problem of attributes. In this paper, knowledge reduction is defined from the view of information, a heuristic algorithm based on rough entropy for knowledge reduction is proposed in incomplete information systems, the time complexity of this algorithm is O(|A|2|U|). An illustrative example is provided that shows the application potential of the algorithm.

scholarly journals Neighborhood Hypergraph Based Classification Algorithm for Incomplete Information System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Feng Hu ◽  
Jin Shi
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Incomplete Information ◽  
Rough Set ◽  
Missing Values ◽  
Rough Set Theory ◽  
Classification Algorithm ◽  
Data Sets ◽  
Incomplete Information System ◽  
Hypergraph Model

The problem of classification in incomplete information system is a hot issue in intelligent information processing. Hypergraph is a new intelligent method for machine learning. However, it is hard to process the incomplete information system by the traditional hypergraph, which is due to two reasons: (1) the hyperedges are generated randomly in traditional hypergraph model; (2) the existing methods are unsuitable to deal with incomplete information system, for the sake of missing values in incomplete information system. In this paper, we propose a novel classification algorithm for incomplete information system based on hypergraph model and rough set theory. Firstly, we initialize the hypergraph. Second, we classify the training set by neighborhood hypergraph. Third, under the guidance of rough set, we replace the poor hyperedges. After that, we can obtain a good classifier. The proposed approach is tested on 15 data sets from UCI machine learning repository. Furthermore, it is compared with some existing methods, such as C4.5, SVM, NavieBayes, andKNN. The experimental results show that the proposed algorithm has better performance via Precision, Recall, AUC, andF-measure.

On Quasi Discrete Topological Spaces in Information Systems

2012 ◽  
Vol 3 (2) ◽  
pp. 38-52 ◽  
Author(s):  
Tutut Herawan
Keyword(s):  
Information System ◽  
Information Systems ◽  
Topological Space ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Topological Spaces ◽  
Discrete Topology

This paper presents an alternative way for constructing a topological space in an information system. Rough set theory for reasoning about data in information systems is used to construct the topology. Using the concept of an indiscernibility relation in rough set theory, it is shown that the topology constructed is a quasi-discrete topology. Furthermore, the dependency of attributes is applied for defining finer topology and further characterizing the roughness property of a set. Meanwhile, the notions of base and sub-base of the topology are applied to find attributes reduction and degree of rough membership, respectively.

scholarly journals Fuzzy Set-Valued Information Systems and the Algorithm of Filling Missing Values for Incomplete Information Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Zhaohao Wang ◽  
Xiaoping Zhang
Keyword(s):  
Information System ◽  
Information Systems ◽  
Incomplete Information ◽  
Rough Set ◽  
Fuzzy Set ◽  
Missing Values ◽  
Rough Set Theory ◽  
Similarity Measures ◽  
New Information ◽  
Incomplete Information Systems

How to effectively deal with missing values in incomplete information systems (IISs) according to the research target is still a key issue for investigating IISs. If the missing values in IISs are not handled properly, they will destroy the internal connection of data and reduce the efficiency of data usage. In this paper, in order to establish effective methods for filling missing values, we propose a new information system, namely, a fuzzy set-valued information system (FSvIS). By means of the similarity measures of fuzzy sets, we obtain several binary relations in FSvISs, and we investigate the relationship among them. This is a foundation for the researches on FSvISs in terms of rough set approach. Then, we provide an algorithm to fill the missing values in IISs with fuzzy set values. In fact, this algorithm can transform an IIS into an FSvIS. Furthermore, we also construct an algorithm to fill the missing values in IISs with set values (or real values). The effectiveness of these algorithms is analyzed. The results showed that the proposed algorithms achieve higher correct rate than traditional algorithms, and they have good stability. Finally, we discuss the importance of these algorithms for investigating IISs from the viewpoint of rough set theory.

An Expanded Feature Extraction of E-Mail Header for Spam Recognition

2013 ◽  
Vol 846-847 ◽  
pp. 1672-1675 ◽  
Author(s):  
Yuan Ning Liu ◽  
Ye Han ◽  
Xiao Dong Zhu ◽  
Fei He ◽  
Li Yan Wei
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Spam Filtering ◽  
Feature Sets ◽  
Filtering Method ◽  
The Past ◽  
Machine Learning Methods ◽  
E Mail

Currently a spam filtering method is extracting attributes from e-mail header and using machine learning methods to classify the sample sets. But as time goes on, spammers transform different ways to send spam, which result in a great change of spam's header. So the attributes defined in the past could not deal with this change sufficiently. This paper extracted attributes from all possible forged header fields to expand the feature sets, then used the rough set theory to classify the sample sets. Experiment validated more attributes including in feature sets may lead to greater performance, in terms of higher recall and precision, lower fake recognition than other algorithms.

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