Kavitha K. R. currently serves as an Assistant Professor at the Department of Computer Science Applications at Amrita School of Engineering, Amritapuri.


Publication Type: Conference Paper

Year of Publication Title


Kavitha K. R., U Harishankar, N., and Akhil, M. C., “PSO Based Feature Selection of Gene for Cancer Classification Using SVM-RFE”, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018, pp. 1012-1016.[Abstract]

Gene expression has a vast area of application in diagnosis of disease in the medical field. In this gene expression data, the number of genes involved is very large (in tenthousand) compared to the number of samples which is very few in cancer classification. This large number of genes in the training sample poses a challenge in cancer classification problem. An effective gene selection system is needed to choose a more relevant gene that plays an important role in cancer classification. Our research focuses on the efficient method of gene selection and cancer classification. An efficient gene selection method is needed to speed up the processing rate and increase the accuracy which in turn decreases the prediction rate. Particle Swarm Optimization (PSO) is used for selecting a subset of important genes which is used as an input for classification using improved Support-Vector Machine-Recursive FeatureElimination (SVM-RFE).

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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.[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.

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Kavitha K. R., Sandeep, S., and Praveen, P. R., “Improved spectral clustering using PCA based similarity measure on different Laplacian graphs”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016.[Abstract]

In data mining, clustering is one of the most significant task, and has been widely used in pattern recognition and image processing. One of the tradition and most widely used clustering algorithm is k-Means clustering algorithm, but this algorithm fails to find structural similarity in the data or if the data is non-linear. Spectral clustering is a graph clustering method in which the nodes are clustered and useful if the data is non-linear and it finds clusters of different shapes. A spectral graph is constructed based on the affinity matrix or similarity matrix and the graph cut is found using Laplacian matrix. Traditional spectral clustering use Gaussian kernel function to construct a spectral graph. In this paper we implement PCA based similarity measure for graph construction and generated different Laplacian graphs for spectral clustering. In PCA based similarity measure, the similarity measure based on eigenvalues and its eigenvectors is used for building the graph and we study the efficiency of two types of Laplacian graph matrices. This graph is then clustered using spectral clustering algorithm. Effect of PCA similarity measure is analyzed on two types of Laplacian graphs i.e., un-normalized Laplacian and normalized Laplacian. The outcome shows accurate result of PCA measure on these two Laplacian graphs. It predicts perfect clustering of non-linear data. This spectral clustering is widely used in image processing.

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Kavitha K. R., Rajendran, G. S., and Varsha, J., “A correlation based SVM-recursive multiple feature elimination classifier for breast cancer disease using microarray”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016.[Abstract]

Support Vector Machine (SVM), is most widely popular learning algorithm used for classification of large dataset. Our project aims to generate a classifier for breast cancer genes microarray by using modified-SVM-RFE algorithm. This breast cancer microarray contains a large number of genes and its expression, so it necessary to reduce the number of genes before applying for classification. So the most efficient algorithm that can be applied for classification of microarray is SVM-RFE, which is an embedded method, which performs backward single gene elimination as well as classification of the dataset. A new modified algorithm is proposed with less computation over SVM-RFE. SVM-RFE generates the rank of the features and eliminates one lowest rank irrelevant feature, in each iteration. Since our microarray contains 47,294 genes its very computational overhead to reduce the dimension. So the modified algorithm which removes more than one irrelevant genes in single iteration of SVM-RFE algorithm. And also this algorithm only removes irrelevant gene, it does not remove the correlated genes. So before applying SVM-RFE, our research focuses on finding out the correlated genes and extracting a new gene from the two, and then apply SVM-RFE on the new set of genes. So our proposed method is Correlation based Support Vector Machine Recursive Multiple Feature Elimination (CSVM-RMFE) algorithm which first extracts a new genes from two correlated genes called virtual gene and then apply SVM-RMFE to generate a classifier. This SVM-RMFE algorithm eliminate multiple feature so that the classification time can be reduced and its accuracy can be increased.

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