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001			vtls000067434
003			KUKTEM
005			20251114204524.0
008			121205t2012 my a f m 000 0 eng d
020			_aTHE0001967(Local)
039		9	_a201905131547 _byusri _c201710161546 _daishah _y201212051628 _zFida
040			_aUMP
090			_aQA76.9.D343 S26 2012 rs Thesis
100	0		_aSanti Puteri Rahayu
245	1	0	_aLogistic regression methods for classification of imbalanced data sets / _cSanti Puteri Rahayu
260			_aKuantan, Pahang : _bUMP, _c2012
300			_axviii, 154 p. : _bill. ; _c30 cm. + _e1 CD-ROM
502			_aThesis (Doctor of Philosophy in Computer Science) -- Universiti Malaysia Pahang - 2012
504			_aBibliography : p. 109-118
520	3		_aClassification of imbalanced data sets is one of the important researches in Data Mining community, since the data sets in many real-world problems mostly are imbalanced class distribution. This thesis aims to develop the simple and effective imbalanced classification algorithms by previously improving the algorithms performance of general classifiers i.e. Kernel Logistic Regression Newton-Raphson (KLR-NR) and Regularized Logistic Regression NR (RLR-NR) which are Logistic Regression (LR)based methods. Both LR-based methods have strong statistical foundation and well known classifiers which have simple solution of unconstrained optimization problem in performing the good performance as well as Support Vector Machine (SVM) which is determined as state-of-the art classifier in Kernel methodology and Data Mining community. However, the imbalanced LR-based methods are not extensively developed such as imbalanced SVM-based methods. Hence, it is required to develop effective imbalanced LR-based methods to be widely used in data mining applications. Numerical results have showed that the use of Truncated Newton method for KLR-NR and RLR-NR which respectively resulted in Newton Truncated Regularized KLR (NTR-KLR) and NTR RLR (NTR-LR), is effective in handling the numerical problems on the huge matrix of linear system of Newton-Raphson update rule i.e. the training time and the singularity problem. These results can be seen as further explanation on the success of Truncated Newton method in TR-KLR and TR Iteratively Re-weighted Least Square (TR-IRLS) algorithm respectively, because of the equivalence of iterative method used by these algorithms. Moreover, only with the use of simple solution of unconstrained optimization problem, numerical results have demonstrated that proposed NTR-KLR and proposed NTR-LR respectively have comparable classification performance with RBFSVM (SVM with Radial Basis Function Kernel). The imbalanced problem of both proposed general classification algorithms which is the limitation of accuracy performance specifically in classifying on the minority class has motivated this research to improve their classification performance on imbalanced data sets. In general, numerical results have showed that the use of adapted Modified AdaBoost methods for NTR-KLR and NTR-LR which respectively resulted in AdaBoost NTR Weighted KLR (AB-WKLR) and AB NTR Weighted RLR (AB-WLR) is significantly successful in improving the accuracy and stability performance of general classifiers i.e. NTR-KLR and NTR-LR respectively. The improvements on both error by g-means and standard deviation of g-means with 5-Fold SCV could be achieved as high as more than 60. Furthermore, numerical results have demonstrated that proposed AB-WKLR and proposed AB-WLR respectively have comparable performances with AdaBoostSVM in classifying imbalanced data sets, only with the use of simple solution of unconstrained weighted optimization problem. Thus, both proposed imbalanced LR-based methods is simple and effective for classification of imbalanced data sets and have promising results.
650		0	_aData mining
856	4	0	_uhttp://ecollib.ump.edu.my/3675/ _zLibrary access only
999			_aVIRTUA40 _c3470 _d3476
999			_aVTLSSORT0080020004000900100024502600300050205040520065008560*9992