Incorporating filtering techniques in a fuzzy linguistic multi-agent model for information gathering on the web

Liu, Xinyue; Herrera, F.; Martínez, Luis; Herrera, Juan Carlos; López, Antonio Gabriel

doi:10.1016/j.fss.2004.03.006

Cited by 92 publications

(23 citation statements)

References 31 publications

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“…TFN is a simple method and easily understood to represent an assessment of decision maker, and fuzzy arithmetic operations in TFN are very easy to do [15]. In addition, the membership function of TFN is considered quite reliable in showing the uncertainty of linguistic assessmentwhich usually represents estimated subjective assessment of decision makers [16]. Uncertainty is one source of measurement errors, which in educational measurement is considered and represented as shown in the Equation…”

Section: Proposed Modelmentioning

confidence: 99%

Decision-Making Model for Student Assessment by Unifying Numerical and Linguistic Data

Andayani

Hartati

Wardoyo

et al. 2017

IJECE

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Learning assessment deals with the process of making a decision on the quality or performance of student achievement in a number of competency standards. In the process, teacher's preferences are provided through both test and non-test, generally in a numeric value, from which the final results are then converted into letters or linguistic value. In the proposed model, linguistic variables are exploited as a form of teacher's preferences in nontest techniques. Consequently, the assessment data set will consist of numerical and linguistic information, so it requires a method to unify them to obtain the final value. A model that uses the 2-tuple linguistic approach and based on matrix operations is proposed to solve the problem. This study proposed a new procedure that consists of four stages: preprocessing, transformation, aggregation and exploitation. The final result is presented in 2-tuple linguistic representation and its equivalent number, accompanied by a description of the achievement of each competency. The α value of 2-tuple linguistic in the final result and in the description of each competency becomes meaningful information that can be interpreted as a comparative ability one student has related to other students, and shows how much potential is achieved to reach higher ranks. The proposed model contributes to enrich the learning assessment techniques, since the exploitation of linguistic variable as representation preferences provides flexible space for teachers in their assessments. Moreover, using the result with respect to students' levels of each competency, students' mastery of each attribute can be diagnosed and their progress of learning can be estimated. Keyword:Multi criteria decision making Student assessment The linguistic 2-tuple Unifying linguistic and numeric values

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Section: Proposed Modelmentioning

confidence: 99%

Decision-Making Model for Student Assessment by Unifying Numerical and Linguistic Data

Andayani

Hartati

Wardoyo

et al. 2017

IJECE

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“…"Incorporating Filtering techniques in a fuzzy linguistic multiagent model for information gathering on the web" [8] provides a new fuzzy linguistic multi-agent model that composes of two information filtering techniques in its structure: a content-based filtering agent and a collaborative filtering agent. These elements are introduced to increase the information filtering possibilities of multi-agent system on the Web toimprove its retrieval issues.…”

Section: Literature Surveymentioning

confidence: 99%

A Fuzzy Logic based Recommender System for E-Learning System with Multi-Agent Framework

Pandey¹,

Singh²

2015

IJCA

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In this paper a multi agent based e-learning framework is proposed which is able to provide a personalized experience to the learner by recommending him study material according to his requirements, goals and calibre. A fuzzy logic based recommender agent framework is used to give further suggestions to learner to increase his/her satisfaction and provide enhanced and personalized learning experience. We also used the Matlab to simulate our recommender agent.

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“…In 102 E. Herrera-Viedma et al present a fuzzy linguistic RS model which incorporates information filtering possibilities in the multi-agent IRS defined in 90 , but now exploiting the advantages of a 2-tuple FLM. This RS is based on a fuzzy linguistic multi-agent model for information gathering on the Web that implements content-based and collaborative information filtering techniques to improve the retrieval process.…”

Section: Fuzzy Linguistic Rss Based On 2-tuple Flmmentioning

confidence: 99%

A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling

Herrera‐Viedma¹,

López-Herrera²

2010

International Journal of Computational Intelligence Systems

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This paper presents a survey of some fuzzy linguistic information access systems. The review shows information retrieval systems, filtering systems, recommender systems, and web quality evaluation tools, which are based on tools of fuzzy linguistic modelling. The fuzzy linguistic modelling allows us to represent and manage the subjectivity, vagueness and imprecision that is intrinsic and characteristic of the processes of information searching, and, in such a way, the developed systems allow users the access to quality information in a flexible and user-adapted way.

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Incorporating filtering techniques in a fuzzy linguistic multi-agent model for information gathering on the web

Cited by 92 publications

References 31 publications

Decision-Making Model for Student Assessment by Unifying Numerical and Linguistic Data

Decision-Making Model for Student Assessment by Unifying Numerical and Linguistic Data

A Fuzzy Logic based Recommender System for E-Learning System with Multi-Agent Framework

A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling

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