Browsing by Author "Amusa L. B."

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    Multiclass Feature Selection and Classification with Support Vector Machine in Genomic Study
    (Professional Statisticians Society of Nigeria (PSSN), 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.
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    On the Approximation of Pareto Distribution to Exponential Distribution Using the Gini Coefficient of Inequality
    (Professional Statisticians Society of Nigeria (PSSN), 2017) Yahya W.B.; Garba M. K.; Amidu L; Olorede, K. O.; Gatta, N. F.; Amusa L. B.
    Pareto proposed that income and wealth distribution obeys a universal power law valid for all times and countries, but subsequent studies have often disputed this position. Some even argued there is indeed no Pareto Law and that it should be entirely discarded in studies on distribution of wealth or resources. Many other probability distributions have been proposed such as log normal, exponential, gamma and two other forms by Pareto himself. Using data on imported goods from the National Bureau of Statistics as a case of distribution of wealth in Nigeria, we demonstrated that the distribution of money spent on importation in Nigeria also follow exponential distribution using the Gini coefficient which is a measure of inequality (degree of concentration) of a variable in the distribution of resources. Simulation studies were carried out at different sizes of items (or households) and varying values of the shape parameter and we compare how close the Gini coefficients of the exponential distribution approximate those obtained from the Pareto data as a credible alternative to Pareto distribution.