Information Aggregation in Intelligent Systems Using Generalized Operators

Rudas, Imre J.; Fodor, János

doi:10.15837/ijccc.2006.1.2272

Cited by 23 publications

(14 citation statements)

References 17 publications

(13 reference statements)

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“…Pointwise extension (similarly to other approaches considered e.g. in [6]) observed in this paper can be successfully applied in this area.…”

Section: Some Remarks On Practical Applicationmentioning

confidence: 83%

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Γ‐agregation Operators and Some Aspects of Generalized Aggregation Problem

Lebedinska

2010

Mathematical Modelling and Analysis

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Abstract. We explore questions related to the aggregation operators and aggregation of fuzzy sets. No preliminary knowledge of the aggregation operators theory and of the fuzzy sets theory are required, because all necessary information is given in Section 2. Later we introduce a new class of γ-aggregation operators, which "ignore" arguments less than γ. Due to this property γ-aggregation operators simplify the aggregation process and extend the area of possible applications. The second part of the paper is devoted to the generalized aggregation problem. We use the definition of generalized aggregation operator, introduced by A. Takaci in [7], and study the pointwise extension of a γ-agop.

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“…Pointwise extension (similarly to other approaches considered e.g. in [6]) observed in this paper can be successfully applied in this area.…”

Section: Some Remarks On Practical Applicationmentioning

confidence: 83%

“…Other interesting, conceptually different methods of generalization can be found in literature, e.g. in [5,6,9] and others.…”

Section: Generalized Aggregationmentioning

confidence: 99%

Γ‐agregation Operators and Some Aspects of Generalized Aggregation Problem

Lebedinska

2010

Mathematical Modelling and Analysis

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“…However, as per Rudas and Fodor [25], rescaling function C to a binary operator on [0, 1], it is possible to obtain a representable uninorm with identity element 0.5 and "as underlying t-norm and t-conorm the product and the probabilistic sum." [25]. This result allows therefore to provide the following definition of C in (−1, 1) and (1, −1):…”

Section: An Alternative Approach -Challengesmentioning

confidence: 99%

Successes and challenges in developing a hybrid approach to sentiment analysis

et al. 2017

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This article covers some success and learning experiences attained during the developing of a hybrid approach to Sentiment Analysis (SA) based on a Sentiment Lexicon, Semantic Rules, Negation Handling, Ambiguity Management and Linguistic Variables. The proposed hybrid method is presented and applied to two selected datasets: Movie Review and Sentiment Twitter datasets. The achieved results are compared against those obtained when Naïve Bayes (NB) and Maximum Entropy (ME) supervised machine learning classification methods are used for the same datasets. The proposed hybrid system attained higher accuracy and precision scores than NB and ME, which shows its superiority when applied to the SA problem at the sentence level. Finally, an alternative strategy to calculating the orientation polarity and polarity intensity in one step instead of the two steps method used in the hybrid approach is explored. The analysis of the yielded mixed results achieved with this alternative approach shows its potential as an aid in the computation of semantic orientations and produced some lessons learnt in developing a more effective mechanism to calculating the orientation polarity and polarity intensity.

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“…Aggregation of information represented by membership functions is a central matter in intelligent systems where fuzzy rule base and reasoning mechanism are applied [6]. Typical examples of such systems consist of, but not limited to, fuzzy control, decision support and expert systems.…”

Section: Intelligent Systems In Engineeringmentioning

confidence: 99%

Computational intelligence for problem solving in engineering

Rudas

Tar

2010

IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society

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Problem solving in engineering is increasingly based on knowledge based methods. The need for reliable, proven, and accepted knowledge makes introduction of computational intelligence in product development slow and problematic. In order to achieve advances in this area, object model definition, object model compatible knowledge definition, application oriented knowledge verification, and computational intelligence methods must be harmonized and coordinated. This paper introduces and discusses possibilities for application of computer science motivated knowledge definition in engineering. The purpose of the reported research is to bring industrial product modeling and intelligent computing theories and methodologies closer. Paper starts with a discussion on characteristics and features of intelligent systems in engineering. Following this, including knowledge in future engineering is defined as placing some elements from human capabilities in a virtual space in order to support tasks in the development and application of products. Next, knowledge and object procedures are identified and attributes of knowledge are defined in order to allow applications in professional engineering environments. Finally, implementation of the proposed intelligent engineering is discussed as its coordination with engineering advising in current product modeling.

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Information Aggregation in Intelligent Systems Using Generalized Operators

Cited by 23 publications

References 17 publications

Γ‐agregation Operators and Some Aspects of Generalized Aggregation Problem

Γ‐agregation Operators and Some Aspects of Generalized Aggregation Problem

Successes and challenges in developing a hybrid approach to sentiment analysis

Computational intelligence for problem solving in engineering

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