Computational intelligence approaches for classification and temporal clustering in bioinformatics
Unprecedented amount of data coming from various high-throughput techniques in biomedical research has presented challenges to computational intelligence (CI) methods to develop efficient algorithms for extraction of knowledge from them. These challenges can be put into one or more of the followin...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/10356/20817 |
| _version_ | 1826110682168295424 |
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| author | Ashish Anand. |
| author2 | Ponnuthurai Nagaratnam Suganthan |
| author_facet | Ponnuthurai Nagaratnam Suganthan Ashish Anand. |
| author_sort | Ashish Anand. |
| collection | NTU |
| description | Unprecedented amount of data coming from various high-throughput techniques
in biomedical research has presented challenges to computational intelligence
(CI) methods to develop efficient algorithms for extraction of knowledge from them. These challenges can be put into one or more of the following different CI perspectives: Feature Selection or feature extraction. Prediction (Classification/Regression). Discovery of new classes (Clustering).
This thesis has studied problems in computational biology domain with above
perspectives and developed efficient CI methods for them. The research work can
be categorized into three parts. First part discusses multi-class classification problems. A framework of class-wise feature selection with One-versus-all-support
vector machine (OVA-SVM) classifier is studied. In this framework three methods
of probability conversion methods of decision function values of SVM and
their ensemble are studied and compared. The proposed framework for multiclass
classification has two distinguishing characteristics. First, it uses class-wise
optimized features and second, decisions of different SVMs classifiers are coupled
with probability estimates to make final prediction. The proposed approach
is used for multi-class classification of several biological datasets. It obtained
better or at least competitive predictive accuracy to the current best methods for
the selected datasets. In all three cases, brief analysis is performed on class-wise
selected features to verify their biological relevance to the associated class. |
| first_indexed | 2024-10-01T02:38:29Z |
| format | Thesis |
| id | ntu-10356/20817 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:38:29Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/208172023-07-04T16:06:04Z Computational intelligence approaches for classification and temporal clustering in bioinformatics Ashish Anand. Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Unprecedented amount of data coming from various high-throughput techniques in biomedical research has presented challenges to computational intelligence (CI) methods to develop efficient algorithms for extraction of knowledge from them. These challenges can be put into one or more of the following different CI perspectives: Feature Selection or feature extraction. Prediction (Classification/Regression). Discovery of new classes (Clustering). This thesis has studied problems in computational biology domain with above perspectives and developed efficient CI methods for them. The research work can be categorized into three parts. First part discusses multi-class classification problems. A framework of class-wise feature selection with One-versus-all-support vector machine (OVA-SVM) classifier is studied. In this framework three methods of probability conversion methods of decision function values of SVM and their ensemble are studied and compared. The proposed framework for multiclass classification has two distinguishing characteristics. First, it uses class-wise optimized features and second, decisions of different SVMs classifiers are coupled with probability estimates to make final prediction. The proposed approach is used for multi-class classification of several biological datasets. It obtained better or at least competitive predictive accuracy to the current best methods for the selected datasets. In all three cases, brief analysis is performed on class-wise selected features to verify their biological relevance to the associated class. Doctor of Philosophy 2010-01-15T03:40:37Z 2010-01-15T03:40:37Z 2010 2010 Thesis http://hdl.handle.net/10356/20817 en 255 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Ashish Anand. Computational intelligence approaches for classification and temporal clustering in bioinformatics |
| title | Computational intelligence approaches for classification and temporal clustering in bioinformatics |
| title_full | Computational intelligence approaches for classification and temporal clustering in bioinformatics |
| title_fullStr | Computational intelligence approaches for classification and temporal clustering in bioinformatics |
| title_full_unstemmed | Computational intelligence approaches for classification and temporal clustering in bioinformatics |
| title_short | Computational intelligence approaches for classification and temporal clustering in bioinformatics |
| title_sort | computational intelligence approaches for classification and temporal clustering in bioinformatics |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | http://hdl.handle.net/10356/20817 |
| work_keys_str_mv | AT ashishanand computationalintelligenceapproachesforclassificationandtemporalclusteringinbioinformatics |