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

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

Granular Reasoning Using Zooming In & Out

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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

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

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

A Note on Granular Reasoning and Semantics of Four-Valued Logic

Proceedings of the Seventh International Conference CASYS\u2705 on Computing Anticipatory Systems, Li?ge, Belgium, August 8-13, 200

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

Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting

Recommender systems (RS) analyze user rating information and recommend items that may interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However, traditional IBCF often cannot provide recommendations with good predictive and classification accuracy at the same time because it assigns equal weights to all items when computing similarity and prediction. However, some items are more relevant and should be assigned greater weight. To address this problem, we propose a niche approach to realize item-variance weighting in IBCF in this paper. In the proposed approach, to improve the predictive accuracy, a novel time-related correlation degree is proposed and applied to form time-aware similarity computation, which can estimate the relationship between two items and reduce the weight of the item rated over a long period. Furthermore, a covering-based rating prediction is proposed to increase classification accuracy, which combines the relationship between items and the target user’s preference into the predicted rating scores. Experimental results suggest that the proposed approach outperforms traditional IBCF and other existing work and can provide recommendations with satisfactory predictive and classification accuracy simultaneously.

Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining

Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, especially when a new item has no rating records (complete new item cold-start). Motivated by this, we propose a niche approach which applies interrelationship mining into IBCF in this paper. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to rich the available information on a new item. Further, similarity, computed using interrelated attributes, can reflect characteristics between new items and others more accurately. Some significant properties, as well as the usage of interrelated attributes, are provided in detail. Experimental results obtained suggest that the proposed approach can effectively solve the complete new item cold-start problem of IBCF and can be used to provide new item recommendations with satisfactory accuracy and diversity in modern RSs.