Publication Type:

Conference Paper

Source:

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Udupi, India (2017)

ISBN:

9781509063673

URL:

https://ieeexplore.ieee.org/document/8125817

Keywords:

Acute Lymphoblastic Leukaemia(ALL), AcuteMyeloid Leukaemia(AML), bioinformatics, cancer, cancer prediction, Classification algorithm, Classification algorithms, correlation-based feature selection, different tumor type, entropy, Fast Correlation-Based Filter, Fast Correlation-Based Filter(FCBF), FCBF, Feature extraction, Feature selection, gene selection, geneexpression, genetics, huge microarray expression, improved svm classifier, Leukaemia cells, leukemia cancer classification, Machine learning algorithms, Pattern classification, Support Vector Machine, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Support vector machines, svm-rfe, Training, tumours

Abstract:

Classification of different tumor type are of great significance in problems cancer prediction. Choosing the most relevant qualities from huge microarray expression is very important. It is a most explored subject in bioinformatics because of its hugeness to move forward humans understanding of inherent causing cancer mechanism. In this paper, we aim to classify leukaemia cells. Our approach relies on selecting the predominant features from the dataset and classifying it using classification algorithm, Support Vector Machine (SVM). As the dataset is very large we need to reduce the size before classification, to decrease the computation time and increase the accuracy of the classifier. SVM-RFE removes only most irrelevant gene in each iteration. This algorithm does not differentiate the correlated genes. So before applying SVM-RFE for gene selection, a correlation-based feature selection is applied using FCBF (Fast Correlation-Based Filter) to select the most prominent not correlated genes. The resultant classifier generated using this set of genes yields more accurate result and reduce the computational time.

Cite this Research Publication

Kavitha K. R., Gopinath, A., and Gopi, M., “Applying Improved SVM Classifier for Leukemia Cancer Classification Using FCBF”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.