Modeling Decisions for Artificial Intelligence

In August 2007, the 4th International Conference on Modeling Decisions for Artificial Intelligence (MDAI1) was held in Kitakyushu, Japan. This special issue has its origins in the conference. We present nine papers related to soft computing tool applications. The first paper, by Honda and Okazaki, presents an axiomatization of a generalized Shaply value. The second paper, by García-Lapresta, also related to decision-making, introduces a multistage decision procedure in which decision-makers opinions are weighted by their contribution to an agreement. The third paper, by Torra and Miyamoto, concerns the problem of loading a container, outlining a system for loading nonorthogonal objects. The fourth paper, by Sakai, Koba, and Nakata, is devoted to rule generation based on rough sets. The fifth paper by Hiramatsu, Huynh, and Nakamori, deals with a fuzzy-based model applied to weather information. The sixth paper, by Inokuchi and Miyamoto, discuss fuzzy clustering algorithms for discrete data. The seventh paper, by Miyamoto, Kuroda, and Arai, studies an algorithm for the sequential extraction of clusters compared to mountain clustering. In the eighth paper, Miyamoto formulates fuzzy clustering using the calculus of variations. The ningth and final paper treats fuzzy clustering, in which Endo et al. discuss fuzzy c-means for data with tolerance. In closing, we thank the referees for their work on reviews and Prof. Hirota for editing this special issue. We also thank the Fuji Technology Press Ltd. staff for its advice. 1Work partially funded by Spanish MEC (projects ARES – CONSOLIDER INGENIO 2010 CSD2007-00004 – and eAEGIS – TSI2007-65406-C03-02)

Download Full-text

Modeling Decisions for Artificial Intelligence

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2007.p0003 ◽

2007 ◽

Vol 11 (1) ◽

pp. 3-3

Author(s):

Vicenç Torra ◽

Yasuo Narukawa ◽

Sadaaki Miyamoto

Keyword(s):

Artificial Intelligence ◽

Fuzzy Clustering ◽

Heuristic Algorithms ◽

Autonomous Mobile Robots ◽

Special Issue ◽

Simultaneous Application ◽

The Third ◽

Appropriate Treatment ◽

E Mail ◽

Selection Of

This special issue presents seven papers that are revised and expanded versions of papers presented at the 2nd International Conference on "Modeling Decisions for Artificial Intelligence" (MDAI). This conference, that took place in Tsukuba (Japan) in July 2005, was the second of the series of MDAI conferences that were initiated in 2004 in Barcelona (Catalonia, Spain). In April 2006, the third edition was held in Tarragona (Catalonia, Spain) and the fourth one is planned in Kitakyushu (Japan) in August 2007. These series of conferences were initiated to foster the use of decision related tools as well as information fusion technologies within artificial intelligence applications. In this issue, we present enhanced version of seven papers presented in the conference. The first paper describes a tool that uses fuzzy logic and neural networks for assigning a treatment to rheumatism. The selection of the appropriate treatment follows oriental medicine. The second paper by Wanyama and Far describes a tool for trade-off analysis to be used in those situations related with decision making in which there is no dominant solution. The third paper is devoted to autonomous mobile robots. The authors describe a multi-layered fuzzy control system for the self-localization of the robot. Two papers devoted to fuzzy clustering follow in this issue. First, one that presents a regularization approach with nonlinear membership weights. One of the proposed methods makes not only possible to perform attraction of data to clusters but also repulsion between different clusters. The second paper on clustering proposes the simultaneous application of homogeneity analysis and fuzzy clustering through the consideration of an appropriate objective function that includes two types of memberships. The sixth paper presents a tool for e-mail classification. The tool brings the name of FIS-CRM that stands for Fuzzy Interrelations and Synonymy Conceptual Representation Model. The issue finishes with a paper on meta-heuristic algorithms for a class of container loading problems. To finish this introduction, we would like to thank the referees for their work on the review process as well as to thank Prof. Hirota, Editor-in-Chief of this journal, for providing us with the opportunity to edit this special issue. The help of Kazuki Ohmori and Kenta Uchino from Fuji Technology Press Ltd. is also acknowledged.

Download Full-text

Data Clustering Algorithms Using Rough Sets

Handbook of Research on Computational Intelligence for Engineering, Science, and Business ◽

10.4018/978-1-4666-2518-1.ch012 ◽

2013 ◽

pp. 297-327 ◽

Cited By ~ 6

Author(s):

B.K. Tripathy ◽

Adhir Ghosh

Keyword(s):

Comparative Study ◽

Rough Set ◽

Fuzzy Clustering ◽

Fuzzy Set ◽

Rough Sets ◽

Data Clustering ◽

Clustering Algorithms ◽

Clustering Methods ◽

Future Studies ◽

Multiple Clusters

Developing Data Clustering algorithms have been pursued by researchers since the introduction of k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were subsequently modified to handle categorical data. In order to handle the situations where objects can have memberships in multiple clusters, fuzzy clustering and rough clustering methods were introduced (Lingras et al 2003, 2004a). There are many extensions of these initial algorithms (Lingras et al 2004b; Lingras 2007; Mitra 2004; Peters 2006, 2007). The MMR algorithm (Parmar et al 2007), its extensions (Tripathy et al 2009, 2011a, 2011b) and the MADE algorithm (Herawan et al 2010) use rough set techniques for clustering. In this chapter, the authors focus on rough set based clustering algorithms and provide a comparative study of all the fuzzy set based and rough set based clustering algorithms in terms of their efficiency. They also present problems for future studies in the direction of the topics covered.

Download Full-text

FEATURE WEIGHTING FUZZY CLUSTERING INTEGRATING ROUGH SETS AND SHADOWED SETS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001412500103 ◽

2012 ◽

Vol 26 (04) ◽

pp. 1250010 ◽

Cited By ~ 11

Author(s):

LINA WANG ◽

JIANDONG WANG

Keyword(s):

Fuzzy Sets ◽

Fuzzy Clustering ◽

Rough Sets ◽

Granular Computing ◽

Clustering Algorithms ◽

Noisy Data ◽

Real Data ◽

Feature Weighting ◽

Novel Method ◽

Shadowed Sets

Associating features with weights is a common approach in clustering algorithms and determining the weight values is crucial in generating valid partitions. In this paper, we introduce a novel method in the framework of granular computing that incorporates fuzzy sets, rough sets and shadowed sets, and calculates feature weights automatically. Experiments on synthetic and real data patterns show that our algorithms always converge and are more effective in handling overlapping among clusters and more robust in the presence of noisy data and outliers.

Download Full-text

Special Issue on Modeling Decisions for Artificial Intelligence

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2011.p0055 ◽

2011 ◽

Vol 15 (1) ◽

pp. 55-55

Author(s):

Vicenç Torra ◽

Yasuo Narukawa ◽

Masahiro Inuiguchi

Keyword(s):

Artificial Intelligence ◽

Rough Sets ◽

Missing Values ◽

Uncertain Data ◽

Interval Data ◽

Data Envelopment ◽

Special Issue ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Information Tables

The 6th International Conference on Modeling Decisions for Artificial Intelligence (MDAI) was held at Awaji Island, Japan, from November 30 to December 2, 2009 and was the inspiration for this special issue. The nine selected papers concern soft computing tool applications. The first, by Yoshida, studies the risk analysis of portfolios under uncertainty and gives expressions showing explicit relationships among parameters in a portfolio. The second, by Entani, proposes an efficiency-interval-based measure based on interval data envelopment analysis. The third, by Hamasuna, Endo, and Miyamoto, concerns clustering for data with tolerance and proposes algorithms for this type of data. The fourth, by Endo, Hasegawa, Hamasuna, and Kanzawa, focuses on fuzzy c-means clustering for uncertain data using quadratic regularization. The fifth, by Honda, Notsu, and Ichihashi, also involves clustering, focusing on variable selection/weighting in PCA-guided k-means. The sixth, by Hwang and Miyamoto, covers clustering focusing on kernel fuzzy c-means and some interesting new results. The seventh, by Kanzawa, Endo, and Miyamoto, uses fuzzy c-means in semisupervised fuzzy c-means. The eighth, by Kudo and Murai, is devoted to rough sets, proposing a heuristic algorithm for calculating a relative reduct candidate. The closing contribution, by Kusunoki and Inuiguchi, is also devoted to rough sets, with the authors studying rough set models in information tables with missing values. We thank the referees for their review work, and the Fuji Technology Press Ltd. staff for its encouragement and advice.

Download Full-text

Special Issue: Machine Learning in Design

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060498122011 ◽

1998 ◽

Vol 12 (2) ◽

pp. 93-94 ◽

Cited By ~ 3

Author(s):

ALEX H.B. DUFFY ◽

DAVID C. BROWN ◽

ASHOK K. GOEL

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Special Issue ◽

The Third

This issue of AIEDAM is based on a workshop on Machine Learning in Design held at the 1996 Conference on Artificial Intelligence in Design, AID'96 (Gero & Sudweeks, 1996), the third of such workshops, with the previous two being held at AID'92 (Gero, 1992) and at AID'94 (Gero & Sudweeks, 1994). The first two workshops also resulted in special issues of AIEDAM (Maher et al., 1994; Duffy et al., 1996).

Download Full-text