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Variable Precision Bayesian Rough Set Model and Its Application to Kansei Engineering
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11548669_31openalex-snapshot
Variable Precision Bayesian Rough Set Model and Its Application to Human Evaluation Data
inferredcites →https://doi.org/10.1007/11548669_33openalex-snapshot
Rough Membership and Bayesian Confirmation Measures for Parameterized Rough Sets
inferredcites →https://doi.org/10.1007/3-540-39205-x_24openalex-snapshot
Evaluation of Probabilistic Decision Tables
inferredcites →https://doi.org/10.1007/3-540-39205-x_46openalex-snapshot
Variable Precision Bayesian Rough Set Model
inferredcites →https://doi.org/10.1007/3-540-45554-x_3openalex-snapshot
Rough Sets and Decision Algorithms
inferredcites →https://doi.org/10.1007/978-0-387-21606-5openalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1007/978-3-540-24681-7_11openalex-snapshot
Computing Minimal Probabilistic Rules from Probabilistic Decision Tables: Decision Matrix Approach
inferredcites →https://doi.org/10.1007/978-3-540-25929-9_46openalex-snapshot
The Rough Bayesian Model for Distributed Decision Systems
inferredcites →https://doi.org/10.1007/978-3-540-48061-7_1openalex-snapshot
Decision Rules, Bayes’ Rule and Rough Sets
inferredcites →https://doi.org/10.1007/978-3-540-48061-7_6openalex-snapshot
Discovery of Rules about Complications
inferredcites →https://doi.org/10.1007/978-3-540-72530-5openalex-snapshot
Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
inferredcites →https://doi.org/10.1007/978-3-540-88425-5openalex-snapshot
Rough Sets and Current Trends in Computing
inferredcites →https://doi.org/10.1007/978-3-7908-1840-6openalex-snapshot
Rough Set Methods and Applications
inferredcites →https://doi.org/10.1007/978-3-7908-1840-6_5openalex-snapshot
Knowledge Discovery by Application of Rough Set Models
inferredcites →https://doi.org/10.1007/978-3-7908-1883-3openalex-snapshot
Rough Sets in Knowledge Discovery 2
inferredcites →https://doi.org/10.1007/b72293openalex-snapshot
New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
inferredcites →https://doi.org/10.1007/b97883openalex-snapshot
Advances in Web intelligence : second International Atlantic Web Intelligence Conference, AWIC 2004, Cancun, Mexico, May 16-19, 2004 : proceedings
inferredcites →https://doi.org/10.1016/0022-0000(93)90048-2openalex-snapshot
Variable precision rough set model
inferredcites →https://doi.org/10.1016/j.ijar.2004.11.004openalex-snapshot
The investigation of the Bayesian rough set model
inferredcites →https://doi.org/10.1080/09528130512331315864openalex-snapshot
Measuring expected effects of interventions based on decision rules
inferredcites →https://doi.org/10.1177/106480460201000203openalex-snapshot
Kansei Engineering in Consumer Product Design
inferredcites →https://doi.org/10.5057/kei.5.4_41openalex-snapshot
ACQUISITION OF KANSEI DECISION RULES OF COFFEE FLAVOR USING ROUGH SET METHOD
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-14815-7_10openalex-snapshot
Three Valued Representation of Opinions in Affective Design
inferred← citeshttps://doi.org/10.1007/978-3-319-60495-4_38openalex-snapshot
History of Kansei Engineering and Application of Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/978-981-33-6652-7_6openalex-snapshot
Affective Design Using Social Big Data
inferred← citeshttps://doi.org/10.1016/j.ergon.2008.11.003openalex-snapshot
A rough set based decision support approach to improving consumer affective satisfaction in product design
inferred← citeshttps://doi.org/10.1016/j.ergon.2013.11.005openalex-snapshot
A product configuration analysis method for emotional design using a personal construct theory
inferred← citeshttps://doi.org/10.1016/j.eswa.2007.09.041openalex-snapshot
A dominance-based rough set approach to Kansei Engineering in product development
inferred← citeshttps://doi.org/10.1016/j.eswa.2012.01.065openalex-snapshot
A multi-objective genetic algorithm approach to rule mining for affective product design
inferred← citeshttps://doi.org/10.1016/j.ijar.2011.12.006openalex-snapshot
Bayesian rough set model: A further investigation
inferred← citeshttps://doi.org/10.1016/j.ijar.2015.01.005openalex-snapshot
Monotonic uncertainty measures for attribute reduction in probabilistic rough set model
inferred← citeshttps://doi.org/10.1016/j.ins.2022.07.048openalex-snapshot
Boundary region-based variable precision covering rough set models
inferred← citeshttps://doi.org/10.1080/0951192x.2018.1526412openalex-snapshot
Affective design using machine learning: a survey and its prospect of conjoining big data
inferred← citeshttps://doi.org/10.1080/09544828.2018.1475629openalex-snapshot
A multi-objective PSO approach of mining association rules for affective design based on online customer reviews
inferred← citeshttps://doi.org/10.1080/14783363.2018.1486548openalex-snapshot
Application of affective engineering to service industries: feelings from airlines’ in-flight service elements
inferred← citeshttps://doi.org/10.1108/17542730810881285openalex-snapshot
Perspectives and the new trend of Kansei /affective engineering
inferred← citeshttps://doi.org/10.1108/17542730810881366openalex-snapshot
Affective design of waiting areas in primary healthcare
inferred← citeshttps://doi.org/10.1109/icmimt.2018.8340429openalex-snapshot
Evaluations of parameters importance based on human sensory data and Bayesian rough set model
inferred← citeshttps://doi.org/10.11606/t.18.2016.tde-11102016-100724openalex-snapshot
Definição do campo das propriedades em aplicações de sistema de engenharia Kansei utilizando inputs de consumidores em lojas virtuais
inferred← citeshttps://doi.org/10.1201/ebk1439821336-11openalex-snapshot
Rough Set Theory and Kansei/Affective Engineering
inferred← citeshttps://doi.org/10.1243/09596518jsce161openalex-snapshot
Kansei engineering and application of the rough sets model
inferred← citeshttps://doi.org/10.14445/22315381/ijett-v24p223openalex-snapshot
New Approach for selecting the most Desirable Options in a Learning Setting
inferred← citeshttps://doi.org/10.2991/cisia-15.2015.126openalex-snapshot
Category Scores for Selecting the Most Desirable Options in a Learning Setting
inferred← citeshttps://doi.org/10.3233/jifs-192032openalex-snapshot
Aesthetic product design combining with rough set theory and fuzzy quality function deployment
inferred← citeshttps://doi.org/10.32657/10356/38583openalex-snapshot
Decision support system for front-end product development
inferred← citeshttps://doi.org/10.46465/endustrimuhendisligi.788310openalex-snapshot
A RULE-BASED APPROACH TO SOFA DESIGN WITH KANSEI ENGINEERING
inferred← citeshttps://doi.org/10.5057/jjske.8.471openalex-snapshot
IDENTIFICATION OF CUSTOMERS KANSEI NEEDS AND DESIGN PATTERN BY ROUGH SET APPROACH
inferred← citeshttps://doi.org/10.5057/kansei.8.1_24openalex-snapshot
ROUGH SET MODEL AS KANSEI ENGINEERING TECHNOLOGY AND ITS APPLICATIONS
inferred← citeshttps://doi.org/10.5821/dissertation-2117-94644openalex-snapshot
Statistical methods in Kansei engineering studies
inferred← citeshttps://doi.org/10.6092/unina/fedoa/3471openalex-snapshot
A Statistical Thinking Approach to Kansei Engineering for Product Innovation
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