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008			201123t2 2 m a|||fram|| 000 0 eng d
020			_aTHE0008992(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aFKOM .A44 2020 r Thesis
100	1		_aAl-Hroob, Essam Muslem Harb, _eauthor.
245	1	0	_aFlexible enhanced fuzzy min–max neural network model for pattern classification problems / _cEssam Muslem Harb Al-Hroob
264			_aKuantan, Pahang : _bUMP, _c2020
264			_c© 2020
300			_axiv, 137 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2020
504			_aIncludes bibliographical references
520	3		_aIn the attempts of building an efficient classifier model, various hybrid computational intelligence models have been introduced. Among these, the enhanced fuzzy min-max (EFMM) model was one of the most recent models coming with many essential features like the ability to provide online learning processes and handling the forgetting problem. Although EFMM has been proven to be one of the most premier models for undertaking the pattern classification problems, issues related to its learning process, concerning the overlap between the hyperboxes, random expansion coefficient value (user-defined) and hyperbox contraction remain unsolved. Therefore, two stages of improvements are introduced in this research to overcome the current limitations and improve classification performance in terms of accuracy and complexity. In the first stage, a new flexible enhanced fuzzy min-max (FEFMM) model is proposed to overcome limitations related to accuracy issue. Hence, four new procedures are introduced. First, a new training strategy to avoid generating unnecessary overlapped regions. Second, a new flexible expansion procedure to replace the expansion coefficient user-defined parameter with a self-adaptive value to produce more accurate decision boundaries. Third, a new overlap test rule is applied during the testing phase to identify any possible containment overlap case and activate the contraction process (if necessary). Fourth, a new contraction procedure to overcome the containment overlap and avoiding the data distortion problem (missing hyperbox information). In the second stage, a new pruning strategy is proposed to further enhance the performance of the proposed model in regards to overcome the network complexity problem. Hence, the resulting model is known as FEFMM-based pruning strategy (FEFMM-PS). The usefulness of both stages is evaluated systematically using a series of experiments using several benchmark datasets. Sixteen data sets are used in the evaluation process. These data sets are obtained from the UCI machine learning repository and the selection of these data sets is related to cover examples of different levels of difficulties, input and output classes, features, and a number of instances. The performance of FEFMM-PS in these experiments are then quantified using statistical measures where the bootstrap and k-fold cross-validation methods have been adopted. The results demonstrate the efficiency of FEFMM in handling pattern classification problems and providing a superior performance of classification accuracy as compared to the other network structures from the same variants such as EFMM, FMM variants and also non-FMM related models. Concerning the FEFMM-PS, the finding reveals that the model (FEFMM-PS) is able to solve network complexity problem and presents better classification accuracy as compared to FEFMM and other models from the literature. The proposed models FEFMM and FEFMM-PS can be applied in several application areas to further assess their applicability, such as face recognition, speaker recognition, signature recognition, and text classification.
610	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses
942			_2lcc _cTHESIS