An Attempt of Object Reduction in Rough Set Theory

Attribute reduction is a popular topic in rough set theory; however, object reduction is not considered popularly. In this paper, from a viewpoint of computing all relative reducts, we introduce a concept of object reduction that reduces the number of objects as long as possible with keeping the results of attribute reduction in the original decision table.INSPEC Accession Number: 1867432

Investigations of Interests that are Induced by Remarkers and their Remarks for Item Advertisements Based on Influencer\u27s Recommendation

In order to sell items of a genre that each user is not interested in, their latent interests of the genre have to be induced. Therefore, we focus on "Influencer," who is the person that has a large impact on a user\u27s behaviors, and research a recommender system which advertises some items by utilizing influencer\u27s remarks via social media. This paper investigates what kinds of remarks and remarkers would induce users\u27interests. As the result, we have revealed many findings. One is that "One of the factors that would induce the users\u27 positive interests on the movie, is the positive reputation for the movie that is included in tweets." The other is that "If the user does not like the remarker, the user\u27s negative interests for the items in the remarks of the person would be induced, even though the person has famousness."INSPEC Accession Number: 1867430

A heuristic method for discovering biomarker candidates based on rough set theory

We apply a combined method of heuristic attribute reduction and evaluation of relative reducts in rough set theory to gene expression data analysis. Our method extracts as many relative reducts as possible from the gene-expression data and selects the best relative reduct from the viewpoint of constructing useful decision rules. Using a breast cancer dataset and a leukemia dataset, we evaluated the classification accuracy for the test samples and biological meanings of the rules. As a result, our method presented superior classification accuracy comparable to existing salient classifiers. Moreover, our method extracted interesting rules including a novel biomarker gene identified in recent studies. These results indicate the possibility that our method can serve as a useful tool for gene expression data analysis

A granularity-based framework of deduction, induction, and abduction

AbstractIn this paper, we propose a granularity-based framework of deduction, induction, and abduction using variable precision rough set models proposed by Ziarko and measure-based semantics for modal logic proposed by Murai et al. The proposed framework is based on α-level fuzzy measure models on the basis of background knowledge, as described in the paper. In the proposed framework, deduction, induction, and abduction are characterized as reasoning processes based on typical situations about the facts and rules used in these processes. Using variable precision rough set models, we consider β-lower approximation of truth sets of nonmodal sentences as typical situations of the given facts and rules, instead of the truth sets of the sentences as correct representations of the facts and rules. Moreover, we represent deduction, induction, and abduction as relationships between typical situations

Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"

Affected by urbanization, centralization and the decrease of overall population, Japan has been making efforts to revitalize the rural areas across the country. One particular effort is to increase tourism to these rural areas via regional branding, using local farm products as tourist attractions across Japan. Particularly, a program subsidized by the government called Michinoeki, which stands for 'roadside station', was created 20 years ago and it strives to provide a safe and comfortable space for cultural interaction between road travelers and the local community, as well as offering refreshment, and relevant information to travelers. However, despite its importance in the revitalization of the Japanese economy, studies with newer technologies and methodologies are lacking. Using sales data from establishments in the Kyushu area of Japan, we used Support Vector to classify content from Twitter into relevant topics and studied their causal relationship to the sales for each establishment using LiNGAM, a linear non-gaussian acyclic model built for causal structure analysis, to perform an improved market analysis considering more than just correlation. Under the hypotheses stated by the LiNGAM model, we discovered a positive causal relationship between the number of tweets mentioning those establishments, specially mentioning deserts, a need for better access and traf^ic options, and a potentially untapped customer base in motorcycle biker groups

Gene Expression Data Analysis Using Heuristic Attribute Reduction in Rough Set Theory

Proceedings of Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems in Okayama on December 8-12, 2010 (SCIS & ISIS 2010

A Heuristic Algorithm for Generating Decision Rules in Variable Precision Rough Set Models

Proceedings of Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems in Okayama on December 8-12, 2010 (SCIS & ISIS 2010

A Modal Characterization of Granular Reasoning Based on Scott - Montague Models

Proceedings of Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems in Nagoya on September 17-21 2008 (SCIS & ISIS 2008

Interrelationship Mining from a Viewpoint of Rough Sets on Two Universes

We discuss connections between the interrelationship mining, proposed by the authors, and rough sets on two universes. The interrelationship mining enable us to extract characteristics based on comparison between values of different attributes. Rough sets on two universes is an theoretical extension of the original rough sets by considering connection between two universes. In this paper, we point out that interrelationship between different attributes in the interrelationship mining is representable by a variant of rough sets on two universes

Rough-Set-Based Interrelationship Mining for Incomplete Decision Tables

Rough-set-based interrelationship mining enables to extract characteristics by comparing the values of the same object between different attributes.To apply this interrelationship mining to incomplete decision tables with null values, in this study, we discuss the treatment of null values in interrelationships between attributes. We introduce three types of null values for interrelated condition attributes and formulate a similarity relation by such attributes with these null values