Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models

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dc.contributor.author Imam, Ayad Tareq
dc.date.accessioned 2010-06-23T10:47:45Z
dc.date.available 2010-06-23T10:47:45Z
dc.date.issued 2010
dc.identifier.uri http://hdl.handle.net/2086/3909
dc.description.abstract There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data. en
dc.language.iso en en
dc.publisher De Montfort University en
dc.subject relative fuzzy en
dc.subject uncertainty en
dc.subject ambiguity en
dc.subject fuzzy logic en
dc.subject neural network en
dc.title Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models en
dc.type Thesis or dissertation en
dc.publisher.department Faculty of Technology en
dc.publisher.department Software Technology Research Laboratory (STRL) en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en


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