- author = "Abdul Majid",
- title = "Optimization and Combination of Classifiers Using Genetic Programming",
- school = "Ghulam Ishaq Khan Institute of Engineering Sciences \& Technology",
- year = "2006",
- address = "Topi, Swabi, NWFP, Pakistan",
- month = may,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://www.hec.gov.pk/english/services/students/PCD/Pages/PCD.aspx?Paged=TRUE&p_ID=876",
- broken = "http://bpt.hec.gov.pk/2511/",
-
URL = "
http://prr.hec.gov.pk/Thesis/349S.pdf",
- size = "155 pages",
-
abstract = "The success of pattern classification system depends
on the improvement of its classification stage. The
work of thesis has investigated the potential of
Genetic Programming (GP) search space to optimise the
performance of various classification models. In this
thesis, two GP approaches are proposed. In the first
approach, GP is used to optimize the performance of
individual classifiers. The performance of linear
classifiers and nearest neighbour classifiers is
improved during GP evolution to develop a high
performance numeric classifier. In second approach,
component classifiers are trained on the input data and
their predictions are extracted. GP search space is
then used to combine the predictions of component
classifiers to develop an optimal composite classifier
(OCC). This composite classifier extracts useful
information from its component classifiers during
evolution process. In this way, the decision space of
composite classifier is more informative and
discriminant. Effectiveness of GP combination technique
is investigated for four different types of
classification models including linear classifiers,
support vector machines (SVMs) classifiers, statistical
classifiers and instance based nearest neighbour
classifiers.
The successfulness of such composite classifiers is demonstrated by performing various experiments, while using Receiver Operating Characteristics (ROC) curve as the performance measure. It is evident from the experimental results that OCC outperforms its component classifiers. It attains high margin of improvement at small feature sets. Further, it is concluded that classification models developed by heterogeneous combination of classifiers have more promising results than their homogeneous combination.
GP optimisation technique automatically caters the selection of suitable component classifiers and model selection. Two main objectives are achieved, while using GP optimisation. First, objective achieved is the development of more optimal classification models. The second one is the enhancement in the GP search strategy itself.",
-
notes = "Serial/PCD.No 875
Item Type: Thesis (PhD)
ID Code: 2511 Deposited By: Ch Abdulla fayyaz Chattha Last Modified: 28 Jul 2009 21:16",
