Browsing by Author "Oyebanji, L. A."

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    Classification into Two Groups with different Cost of Misclassification Ratios
    (Nigerian Association of Mathematical Physics, 2016) Oyeyemi, G. M.; Oyebanji, L. A.
    Fisher’s Linear Discriminant and Bayesian Classification procedures were compared when the assumption of equal cost of misclassification is violated. The comparison was carried out at various sample sizes and different misclassification cost ratios. Data were simulated to consist two groups (populations) of four variables each from two multivariate normal populations. The homogeneity of the variance-covariance matrices of the two groups was tested using Box’s M-Test. The apparent Error Rate as the estimate of the Actual Error Rate was used to judge the performance of both procedures at different misclassification cost ratios (1:1, 1:2, , , 4:5) and sample sizes (10, 20, 30, 40, , , 100). The results show that at equal cost ratio (1:1), both approaches produced almost the same error rate at different sample sizes. With difference in misclassification cost ratio, the Bayesian approach generally has higher proportion of misclassification than the Fisher at various ratios and sample sizes. The Fisher performed better in small sample cases (n < 50) under all cost ratios considered except 1:2 and 1:5. For large sample cases (n > 50), the performance was better at cost ratios 2:3, 2:4 and 2:5
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    Comparison between Fisherian and Bayesian Approach to Classification Using two Groups
    (College of Natural and Applied Science, University of Porthacourt, Nigeria, 2014) Oyeyemi, G. M.; Oyebanji, L. A.; Salau, I. S.; Folorunsho, A. I
    Two approaches to discriminant analysis procedure are examined and compared based on their misclassification error rate. The Fisher's approach tends to find a linear combination of the variables which maximize the ratio of the between group sum of squares to that of the within group sum of squares in achieving a good separation. On the other hand, the Bayesian approach assigns an observed unit to a group with the greatest posterior probability. Fisher's linear discriminant analysis though is the most widely used method of classification because of its simplicity, and optimality properties is normally used for two group cases. However, Bayesian approach is found to be better than Fisher's approach because of its low misclassification error rate.