Browsing by Author "Garba, M. K"
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Item Efficient Support Vector Machine Classification of Diffuse Large B-Cell Lymphoma and Follicular Lymphoma mRNA Tissue Samples(Faculty of Computer and Applied Computer Science, Tibiscus University of Timisoara, Romania., 2015) Banjoko, A. W.; Yahya, W. B.; Garba, M. K; Olaniran, O. R.; Dauda, K. A.; Olorede, K. O.In this study, an efficient Support Vector Machine (SVM) algorithm that incorporates feature selection procedure for efficient identification and selection of gene biomarkers that are predictive of Diffuse Large B–Cell Lymphoma (DLBCL) and Follicular Lymphoma (FL) cancer tumor samples is presented. The data employed were published real life microarray cancer data that contained 7,129 gene expression profiles measured on 77 biological samples that comprised 58 DLBCL and 19 FL tissue samples. The dimension reduction approach of the Welch statistic was employed at the feature selection phase of the SVM algorithm. The cost and kernel parameters of the SVM model were tuned over a 10–fold cross-validation to improve the efficiency of the SVM classifier. The entire sample was randomly partitioned into 95% training and 5% test samples. The SVM classifier was trained using Monte Carlo Crossvalidation approach with 1000 replications. The performance of this classifier was assessed on the test samples using misclassification error rate (MER) and other performance measures. The results showed that the SVM classifier is quite efficient by yielding very high prediction accuracy of the tumor samples with fewer differentially expressed genes. The selected gene biomarkers in this work can be subjected to further clinical screening for proper determination of their biological relationship with DLBCL and FL tumour subgroups. However, more studies with large samples might be needed in future to validate the results from this workItem Multiclass Feature Selection and Classification with Support Vector Machine in Genomic Study(Edited Conference Proceedings of the 1st International Conference of the Nigeria Statistical Society (NSS)., 2017) Banjoko, A. W.; Yahya, W. B.; Garba, M. K; Olaniran, O. R.; Amusa, L. B.; Gatta, N. F.; Dauda, K. A.; Olorede, K. O.This study proposes an efficient Support Vector Machine (SVM) algorithm for feature selection and classification of multiclass response group in high dimensional (microarray) data. The Feature selection stage of the algorithm employed the F-statistic of the ANOVA–like testing scheme at some chosen family-wise-error-rate (FWER) to control for the detection of some false positive features. In a 10-fold cross validation, the hyper-parameters of the SVM were tuned to determine the appropriate kernel using one-versus-all approach. The entire simulated dataset was randomly partitioned into 95% training and 5% test sets with the SVM classifier built on the training sets while its prediction accuracy on the response class was assessed on the test sets over 1000 Monte-Carlo cross-validation (MCCV) runs. The classification results of the proposed classifier were assessed using the Misclassification Error Rates (MERs) and other performance indices. Results from the Monte-Carlo study showed that the proposed SVM classifier was quite efficient by yielding high prediction accuracy of the response groups with fewer differentially expressed features than when all the features were employed for classification. The performance of this new method on some published cancer data sets shall be examined vis-à-vis other state-of-the-earth machine learning methods in future works.Item Structural Relationships of Exchange Rates of Naira to Some Foreign Currencies(Edited Conference Proceedings of the 1st International Conference of the Nigeria Statistical Society (NSS)., 2017) Garba, M. K; Yahya, W. B.; Babaita, H. T.; Banjoko, A. W.; Amobi, A. Q.This study investigates the existence of causality among exchange rates of Naira to three of the major foreign currencies (Euro, Pound Sterling and US Dollar). The work is aimed at determining the patterns of causalities that exist among these three foreign currencies to Nigerian Naira using multivariate time series modelling techniques. The data employed for this study were on daily exchange rates of Naira to Euro, Pound Sterling and US Dollar over a period of thirteen years beginning from 1st January 2002 to 31st December, 2014. The rates were national datasets extracted from the published statistical bulletin of the Central Bank of Nigeria. The Vector Autoregressive (VAR) model which is useful for describing the dynamic behavior of economic and financial time series was fitted to the data. The potential causal relationships among the three exchange rates using the Granger Causality tests were examined. Results revealed that the future exchange rates of Naira to Euro can be predicted by the past values of Naira to Euro and Naira to US Dollar. Finally, the exchange rates of Naira to Pound Sterling was granger caused by Naira to Euro and Naira to US Dollar exchange rates, and the rate of exchange of Naira to US Dollar was granger caused by the Naira to Euro exchange rates. Results from this work would assist the government, policy makers and other interested stakeholders to be familiar with the inherent relationship among the notable currencies to the Naira for efficient business decisions.