Abstract
Recently, Gene expression profiling by microarray technique has been effectively utilized for classification and diagnostic guess- ing of cancer nodules in the field of medical sciences. But the techniques used for cancer classification is still in its lower level. There are various drawbacks in the existing classification techniques such as low testing accuracy, high training time, unreliability, etc. Moreover, mi- croarray data consists of a high degree of noise. Gene ranking techniques such as T-Score, ANOVA, etc are later proposed to overcome those problems. But those approaches will sometimes wrongly predict the rank when large database is used. To overcome these issues, this paper mainly focuses on the development of an effective feature selection and classification technique for microarray gene expression cancer diagnosis for provide significant accuracy, reliability and less error rate. In this paper, Wrapper feature selection approach called the GA-FSVML approach is used for the effective feature selection of genes. In FSVML, the RBF kernel function in SVM is trained using modi- fied Levenberg Marquadt algorithm. This approach proposes a Fast SVM Learning (FSVML) technique for the classification tasks. The ex- periment is performed on lymphoma data set and the result shows the better accuracy of the proposed FSVML with GA-FSVML classifica- tion approach when compared to the standard existing approaches.
Highlights
Microarray data analysis has been extensively employed in several analysis over a extensive range of biological domains which consists of cancer classification by class detection and prediction, recognition of the unknown effects of a particular therapy, cancer diagnosis, etc [1,2]
In the lymphoma data set, there are 42 samples obtained from Diffuse Large B-cell Lymphoma (DLBCL) [20], nine samples from Follicular Lymphoma (FL) and 11 samples from Chronic Lymphocytic Leukemia (CLL)
The genes are passed to the Fast Support Vector Machine (SVM) Learning (FSVML) classifier for classification
Summary
Microarray data analysis has been extensively employed in several analysis over a extensive range of biological domains which consists of cancer classification by class detection and prediction, recognition of the unknown effects of a particular therapy, cancer diagnosis, etc [1,2]. An application field where these methods are likely to create key contributions is the identification of cancers depends on clinical phase and biological activities. Such classifications have a huge contribution on diagnosis and treatment. Various recent investigations in the field of microarray have discussed the application of feature selection approaches to highdimensional datasets. These feature selection approaches can be used to choose smaller subsets of interesting genes, supporting the analysis of statistical models while keeping the highest possible degree of the accuracy of models developed on the full dataset [5]. Filter techniques generally rank each gene individually by certain quality criterion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
