5,764 research outputs found
Mining Target-Oriented Fuzzy Correlation Rules to Optimize Telecom Service Management
To optimize telecom service management, it is necessary that information
about telecom services is highly related to the most popular telecom service.
To this end, we propose an algorithm for mining target-oriented fuzzy
correlation rules. In this paper, we show that by using the fuzzy statistics
analysis and the data mining technology, the target-oriented fuzzy correlation
rules can be obtained from a given database. We conduct an experiment by using
a sample database from a telecom service provider in Taiwan. Our work can be
used to assist the telecom service provider in providing the appropriate
services to the customers for better customer relationship management.Comment: 10 pages, 7 table
Emotional Design: An Overview
Emotional design has been well recognized in the domain of human factors and ergonomics. In this chapter, we reviewed related models and methods of emotional design. We are motivated to encourage emotional designers to take multiple perspectives when examining these models and methods. Then we proposed a systematic process for emotional design, including affective-cognitive needs elicitation, affective-cognitive needs analysis, and affective-cognitive needs fulfillment to support emotional design. Within each step, we provided an updated review of the representative methods to support and offer further guidance on emotional design. We hope researchers and industrial practitioners can take a systematic approach to consider each step in the framework with care. Finally, the speculations on the challenges and future directions can potentially help researchers across different fields to further advance emotional design.http://deepblue.lib.umich.edu/bitstream/2027.42/163319/1/Emotional_Design_Manuscript_Final.pdfSEL
A logic approach for exceptions and anomalies in association rules
Association rules have been used for obtaining information hidden in a
database. Recent researches have pointed out that simple associations are
insu cient for representing the diverse kinds of knowledge collected in a
database. The use of exceptions and anomalies deal with a di erent type
of knowledge sometimes more useful than simple associations. Moreover ex-
ceptions and anomalies provide a more comprehensive understanding of the
information provided by a database.
This work intends to go deeper in the logic model studied in [5]. In the
model, association rules can be viewed as general relations between two or
more attributes quanti ed by means of a convenient quanti er. Using this
formulation we establish the true semantics of the distinct kinds of knowledge
we can nd in the database hidden in the four folds of the contingency table.
The model is also useful for providing some measures for assessing the validity
of those kinds of rulesPeer Reviewe
Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation
Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i.e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules. This paper builds on prior work for interval type-2 fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are generated using an initial clustering stage. By introducing Subtractive Clustering in order to identify multiple cluster prototypes, the proposed approach has the potential to deliver improved classification performance while maintaining good interpretability, i.e. without resulting in an excessive number of rules. The paper provides a detailed overview of the proposed FRBC framework, followed by a series of exploratory experiments on both linearly and non-linearly separable datasets, comparing results to existing rule-based and SVM approaches. Overall, initial results indicate that the approach enables comparable classification performance to non rule-based classifiers such as SVM, while often achieving this with a very small number of rules
Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation
Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i.e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules. This paper builds on prior work for interval type-2 fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are generated using an initial clustering stage. By introducing Subtractive Clustering in order to identify multiple cluster prototypes, the proposed approach has the potential to deliver improved classification performance while maintaining good interpretability, i.e. without resulting in an excessive number of rules. The paper provides a detailed overview of the proposed FRBC framework, followed by a series of exploratory experiments on both linearly and non-linearly separable datasets, comparing results to existing rule-based and SVM approaches. Overall, initial results indicate that the approach enables comparable classification performance to non rule-based classifiers such as SVM, while often achieving this with a very small number of rules
Transparency and Reproducibility in Participatory Systems Modelling: the Case of Fuzzy Cognitive Mapping
By aggregating semi-quantitative mind maps from multiple agents, fuzzy cognitive mapping (FCM) allows developing an integrated, cross-sectoral understanding of complex systems. However, and especially for FCM based on individual interviews, the map-building process presents potential pitfalls. These are mainly related to the different understandings of the interviewees about the FCM semantics as well as the biases of the analyst during the elicitation and treatment of data. This paper introduces a set of good practice measures to increase transparency and reproducibility of map-building processes in order to improve credibility of results from FCM applications. The case study used to illustrate the proposed good practices assesses heatwave impacts and adaptation options in an urban environment. Agents from different urban sectors were interviewed to obtain individual cognitive maps. Using this set of data, we suggest good practices to collect, digitalize, interpret, pre-process and aggregate the individual maps in a traceable and coherent way. © 2018 The Authors Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley and Sons Ltd. © 2018 The Authors Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley and Sons LtdThis study is part of the project Bottom-up Climate Adaptation Strategies for a Sustainable Europe (BASE) funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No. 308337. MO (FPDI-2013-16631 and IJCI-2016-28835) and MBN (RYC-2013-13628) acknowledge co-funding from the Spanish Ministry of Economy, Industry and Competitiveness (MINECO)
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
A framework for managing global risk factors affecting construction cost performance
Poor cost performance of construction projects has been a major concern for both
contractors and clients. The effective management of risk is thus critical to the success of any construction project and the importance of risk management has grown as projects have become more complex and competition has increased. Contractors have
traditionally used financial mark-ups to cover the risk associated with construction
projects but as competition increases and margins have become tighter they can no longer rely on this strategy and must improve their ability to manage risk. Furthermore, the construction industry has witnessed significant changes particularly in procurement
methods with clients allocating greater risks to contractors.
Evidence shows that there is a gap between existing risk management techniques and
tools, mainly built on normative statistical decision theory, and their practical application
by construction contractors. The main reason behind the lack of use is that risk decision
making within construction organisations is heavily based upon experience, intuition and
judgement and not on mathematical models.
This thesis presents a model for managing global risk factors affecting construction cost
performance of construction projects. The model has been developed using behavioural
decision approach, fuzzy logic technology, and Artificial Intelligence technology. The
methodology adopted to conduct the research involved a thorough literature survey on
risk management, informal and formal discussions with construction practitioners to
assess the extent of the problem, a questionnaire survey to evaluate the importance of
global risk factors and, finally, repertory grid interviews aimed at eliciting relevant
knowledge. There are several approaches to categorising risks permeating construction projects. This
research groups risks into three main categories, namely organisation-specific, global and
Acts of God. It focuses on global risk factors because they are ill-defined, less
understood by contractors and difficult to model, assess and manage although they have
huge impact on cost performance. Generally, contractors, especially in developing
countries, have insufficient experience and knowledge to manage them effectively. The
research identified the following groups of global risk factors as having significant impact
on cost performance: estimator related, project related, fraudulent practices related,
competition related, construction related, economy related and political related factors.
The model was tested for validity through a panel of validators (experts) and crosssectional
cases studies, and the general conclusion was that it could provide valuable
assistance in the management of global risk factors since it is effective, efficient, flexible
and user-friendly. The findings stress the need to depart from traditional approaches and
to explore new directions in order to equip contractors with effective risk management
tools
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