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Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making. INFORMATION 2020. [DOI: 10.3390/info11040229] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The purpose of this extension of the ESM’2019 conference paper is to propose some means to implement an artificial thinking model that simulates human psychological behavior. The first necessary model is the time fuzzy vector space model (TFVS). Traditional fuzzy logic uses fuzzification/defuzzification, fuzzy rules and implication to assess and combine several significant attributes to make deductions. The originality of TFVS is not to be another fuzzy logic model but rather a fuzzy object-oriented model which implements a dynamic object structural, behavior analogy and which encapsulates time fuzzy vectors in the object components and their attributes. The second model is a fuzzy vector space object oriented model and method (FVSOOMM) that describes how-to realize step by step the appropriate TFVS from the ontology class diagram designed with the Unified Modeling Language (UML). The third contribution concerns the cognitive model (Emotion, Personality, Interactions, Knowledge (Connaissance) and Experience) EPICE the layers of which are necessary to design the features of the artificial thinking model (ATM). The findings are that the TFVS model provides the appropriate time modelling tools to design and implement the layers of the EPICE model and thus the cognitive pyramids of the ATM. In practice, the emotion of cognitive dissonance during buying decisions is proposed and a game addiction application depicts the gamer decision process implementation with TFVS and finite state automata. Future works propose a platform to automate the implementation of TFVS according to the steps of the FVSOOMM method. An application is a case-based reasoning temporal approach based on TFVS and on dynamic distances computing between time resultant vectors in order to assess and compare similar objects’ evolution. The originality of this work is to provide models, tools and a method to design and implement some features of an artificial thinking model.
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Abstract
The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.
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Affiliation(s)
| | - Jędrzej Potoniec
- Institute of Computing Science, Poznan University of Technology, Ponzan, Poland
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Onto-Relational Learning is an extension of Relational Learning aimed at accounting for ontologies in a clear, well-founded and elegant manner. The system -QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach. It takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels. The functionalities of the system are illustrated by means of examples taken from a Semantic Web Mining case study concerning the analysis of relational data extracted from the on-line CIA World Fact Book.
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Abstract
AbstractA unifying framework for concept-learning, derived from Mitchell's Generalization as Search-paradigm, is presented. Central to the framework is the generic algorithm Gencol. Gencol forms a synthesis of existing concept-learning algorithms as it identifies the key issues in concept-learning: the representation of concepts and examples, the search strategy and heuristics, and the operators that transform one concept-description into another one when searching the concept description space. Gencol is relevant for practical purposes as it offers a solid basis for the design and implementation of concept-learning algorithms. The presented framework is quite general as seemingly disparate algorithms such as TDIDT, AQ, MIS and version spaces fit into Gencol.
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Nayak R. Generating rules with predicates, terms and variables from the pruned neural networks. Neural Netw 2009; 22:405-14. [DOI: 10.1016/j.neunet.2009.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2007] [Revised: 10/03/2008] [Accepted: 02/06/2009] [Indexed: 10/21/2022]
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Abstract
BACKGROUND Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. METHODS In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. RESULTS Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. CONCLUSION We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.
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Affiliation(s)
- Aurélie Névéol
- National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
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Function-free Horn clauses are hard to approximate. ACTA ACUST UNITED AC 2005. [DOI: 10.1007/bfb0027323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Learning features by experimentation in chess. ACTA ACUST UNITED AC 2005. [DOI: 10.1007/bfb0017040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Abstract
Data mining algorithms look for patterns in data. While most existing data mining approaches look for patterns in a single data table, multi-relational data mining (MRDM) approaches look for patterns that involve multiple tables (relations) from a relational database. In recent years, the most common types of patterns and approaches considered in data mining have been extended to the multi-relational case and MRDM now encompasses multi-relational (MR) association rule discovery, MR decision trees and MR distance-based methods, among others. MRDM approaches have been successfully applied to a number of problems in a variety of areas, most notably in the area of bioinformatics. This article provides a brief introduction to MRDM, while the remainder of this special issue treats in detail advanced research topics at the frontiers of MRDM.
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Affiliation(s)
- Sašo Džeroski
- Jožef Stefan Institute, Jamova 39, Ljubljana, Slovenia
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Horváth T, Turán G. Learning logic programs with structured background knowledge☆☆An extended abstract of this paper appeared in: L. De Raedt (Ed.), Proceedings of the Fifth International Workshop on Inductive Logic Programming, Tokyo, Japan, 1995, pp. 53–76, Scientific Report of the Department of Computer Science, Katholieke Universiteit Leuven, and also in the post-conference volume: L. De Raedt (Ed.), Advances in Inductive Logic Programming, IOS Press, Amsterdam/Ohmsha, Tokyo, 1996, pp. 172–191. ARTIF INTELL 2001. [DOI: 10.1016/s0004-3702(01)00062-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Morik K, Pigeot I, Robers U. CORA--a knowledge-based system for the analysis of case-control studies. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 1999; 58:35-50. [PMID: 10195645 DOI: 10.1016/s0169-2607(98)00065-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Carrying out a statistical analysis, the researcher is concerned with the problem of choosing an appropriate statistical technique from a large number of competing methods. Most common statistical software offer different methods for analysing the data without giving any support regarding the adequacy of a method for a particular data set. This paper outlines the main features of the computer system CORA which provides a statistical analysis of stratified contingency tables and additionally supports the researcher at the different steps of this analysis. Here, the support given by the system consists of two different aspects. On the one hand, the help system of CORA contains general information on the implemented statistical methods which can be obtained on request. On the other hand, an advice tool recommends an adequate statistical method which depends on the actual empirical case-control data to be analysed. To build up the advice tool, a set of rules being discovered by machine learning from simulation studies is integrated into the system CORA.
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Affiliation(s)
- K Morik
- Department of Computer Science VIII, University of Dortmund, Germany
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Abstract
The paper gives an overview of theoretical results in the rapidly growing field of inductive logic programming (ILP). The ILP learning situation (generality model, background knowledge, examples, hypotheses) is formally characterized and various restrictions of it are discussed in the light of their impact on learnability. The two dominant models of learnability, PAC-learning and identification in the limit, are extended to take into account the ILP learning situation. Several learnability results for logic programs are then presented, both positive and negative.
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Antoniou G. Version space algorithms on hierarchies with exceptions. PROGRESS IN ARTIFICIAL INTELLIGENCE 1993. [DOI: 10.1007/3-540-57287-2_43] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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An incremental deductive strategy for controlling constructive induction in learning from examples. Mach Learn 1991. [DOI: 10.1007/bf00058925] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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BIBLIOGRAPHY OF RECENT MACHINE LEARNING RESEARCH 1985–1989. Mach Learn 1990. [DOI: 10.1016/b978-0-08-051055-2.50033-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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