Topic
Soft computing
About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.
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TL;DR: This special topic section collects research articles witnessing some recent advances in improving the processes of information access and retrieval on the Web by using soft computing tools, and in particular, by using fuzzy sets and integrating them with othersoft computing tools.
Abstract: The World Wide Web is a popular and interactive medium used to collect, disseminate, and access an increasingly huge amount of information, which constitutes the mainstay of the so-called information and knowledge society. Because of its spectacular growth, related to both Web resources (pages, sites, and services) and number of users, the Web is nowadays the main information repository and provides some automatic systems for locating, accessing, and retrieving information. However, an open and crucial question remains: how to provide fast and effective retrieval of the information relevant to specific users' needs. This is a very hard and complex task, since it is pervaded with subjectivity, vagueness, and uncertainty. The expression soft computing refers to techniques and methodologies that work synergistically with the aim of providing flexible information processing tolerant of imprecision, vagueness, partial truth, and approximation. So, soft computing represents a good candidate to design effective systems for information access and retrieval on the Web. One of the most representative tools of soft computing is fuzzy set theory. This special topic section collects research articles witnessing some recent advances in improving the processes of information access and retrieval on the Web by using soft computing tools, and in particular, by using fuzzy sets andsor integrating them with other soft computing tools. In this introductory article, we first review the problem of Web retrieval and the concept of soft computing technology. We then briefly introduce the articles in this section and conclude by highlighting some future research directions that could benefit from the use of soft computing technologies. © 2006 Wiley Periodicals, Inc.
79 citations
TL;DR: Current limitations and challenges are discussed, including advances in network implementations, applications to unconventional resources, dataset acquisition and synthetic training, extrapolative potential, accuracy loss from soft computing, and the computational cost of 3D Deep Learning.
78 citations
Journal Article•
TL;DR: In this survey, the main soft computing methods applied in credit scoring models are presented and the advantages as well as the limitations of each method are outlined.
Abstract: During the last fifteen years, soft computing methods have been successfully applied in building powerful and flexible credit scoring models and have been suggested to be a possible alternative to statistical methods. In this survey, the main soft computing methods applied in credit scoring models are presented and the advantages as well as the limitations of each method are outlined. The main modelling issues are discussed especially from the data mining point of view. The study concludes with a series of suggestions of other methods to be investigated for credit scoring modelling.
78 citations
22 Dec 2008
TL;DR: This book highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms.
Abstract: Providing a thorough introduction to the field of soft computing techniques, Intelligent Systems: Modeling, Optimization, and Control covers every major technique in artificial intelligence in a clear and practical style. This book highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms. The book demonstrates concepts through simulation examples and practical experimental results. Case studies are also presented from each field to facilitate understanding.
78 citations
10 Oct 2004
TL;DR: This work describes an application of rough sets in the fraud detection of electrical energy consumers by derives a set of rules that reaches consumers behavior, allowing the classification rule system to predict many fraud consumers profiles.
Abstract: Rough set is an emergent technique of soft computing that have been used in many knowledge discovery in database applications. This work describes an application of rough sets in the fraud detection of electrical energy consumers. From an information system, rough sets concept of reduct was used to reduce the number of conditional attributes and the minimal decision algorithm (MDA) was used to reduce some values of conditional attributes. The reduced information system derives a set of rules that reaches consumers behavior, allowing the classification rule system to predict many fraud consumers profiles. Rough sets prove that it is a powerful technique with application in many systems based in data.
78 citations