Incremental Fuzzy Decision Trees

Guetova, Marina; Hölldobler, Steffen; Störr, Hans-Peter

doi:10.1007/3-540-45751-8_5

Cited by 21 publications

(10 citation statements)

References 4 publications

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“…Instead of crisp boundaries between categories, fuzzy logic introduces a membership function, which reflects how well a given value falls into a category (see the lower image in Figure 3). On the basis of a fuzzy representation of context (Mantyjarvi and Seppanen, 2002), we use fuzzy decision trees (Janikow, 1996) for inference (see also Zeidler and Schlosser, 1996;Shiu et al, 2000;Guetova et al, 2002). Our rationale for the use of a fuzzy decision tree is its potential to express symbolically the rules governing the system's proactive behaviour.…”

Section: Use Of Fuzzy Logicmentioning

confidence: 99%

Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System

Cheverst

Byun

Fitton

et al. 2005

User Model User-Adap Inter

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It is important that systems that exhibit proactive behaviour do so in a way that does not surprise or frustrate the user. Consequently, it is desirable for such systems to be both personalised and designed in such a way as to enable the user to scrutinise her user model (part of which should hold the rules describing the behaviour of the system). This article describes on-going work to investigate the design of a prototype system that can learn a given user's behaviour in an office environment in order to use the inferred rules to populate a user model and support appropriate proactive behaviour (e.g. turning on the user's fan under appropriate conditions). We explore the tension between user control and proactive services and consider issues related to the design of appropriate transparency with a view to supporting user comprehensibility of system behaviour. To this end, our system enables the user to scrutinise and possibly over-ride the 'IF-THEN' rules held in her user model. The system infers these rules from the context history (effectively a data set generated using a variety of sensors) associated with the user by using a fuzzy-decisiontree-based algorithm that can provide a confidence level for each rule in the user model. The evolution of the system has been guided by feedback from a number of real-life users in a university department. A questionnaire study has yielded supplementary results concerning the extent to which the approach taken meets users' expectations and requirements.

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Section: Use Of Fuzzy Logicmentioning

confidence: 99%

Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System

Cheverst

Byun

Fitton

et al. 2005

User Model User-Adap Inter

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“…They give a hierarchical way to represent rules underlying data [26]. For the automatic construction of decision trees, ID3 or CART [27] can be used.…”

Section: Related Workmentioning

confidence: 99%

Active rule learning using decision tree for resource management in Grid computing

Khanli

Mahan

Isazadeh

2011

Future Generation Computer Systems

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“…Then calculate subordinating degree of all the classifications for the sample using operator S (fuzzy adder ⊕ ). Lastly, deblurring method is used to determine the final classification of the sample [16]. The detailed steps as follows:…”

Section: F Rule Matchingmentioning

confidence: 99%

“…Fuzzy decision tree uses T-S mode classification technology [16]. For the classification of a new sample, firstly, using operator T (fuzzy multiplication ⊗ ) calculate non-zero subordinating degree of all the leaf nodes which belong to a special classification.…”

Section: F Rule Matchingmentioning

confidence: 99%

A Method for Spam Behavior Recognition Based on Fuzzy Decision Tree

Wang

Zhong

2009

2009 Ninth IEEE International Conference on Computer and Information Technology

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Behavior recognition technology can determine whether an email is a spam or ham based on behavior features instead of contents. Statistical behavior features are high degree distinct between spam and ham, and comparatively inherent. This paper proposed a spam behavior recognition model, and developed a Fuzzy Decision Tree based spam filter system. The system computed Information Gain to analyze and select behavior features of emails. The absolutely clear attributes does not always exist in real world. Email features extracted from email messages, and processed by fuzzy processor and data generalization, and more natural and reasonable to describe the characteristics of behavior. According to knowledge of the fuzzy decision tree by Fuzzy-ID3, spam can be detected and classified spam sender behavior patterns can by analyzed automatically.

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Incremental Fuzzy Decision Trees

Cited by 21 publications

References 4 publications

Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System

Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System

Active rule learning using decision tree for resource management in Grid computing

A Method for Spam Behavior Recognition Based on Fuzzy Decision Tree

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