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Extreme Learning Machine (ELM) algorithm assigns the input weights and biases in a “one-time stamp” fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the Moth-Flame Optimization (MFO) algorithm to improve classification accuracy. A hybrid of the Moth-Flame Optimization and Extreme Learning Machine (MFO-ELM) algorithm is implemented in MATLAB. MFO ensures a concurrent simulation of exploration and exploitation of the search space to select an optimum candidate solution. The candidate solution is reshaped into input weights and biases for ELM classification. The hybrid algorithm is validated on five life-selected datasets. The performance improvement of MFO-ELM is compared with ELM-optimized Particle Swarm Optimization (PSO-ELM) and Competitive Swarm Optimization (CSO-ELM) algorithms. The improvement rates are qualitatively and quantitatively evaluated to show the improvement of MFO-ELM on ELM and the other meta-heuristic algorithms. MFO-ELM improved the accuracies of the basic ELM in all 100% of the simulations and performed better than the other meta-heuristic algorithms in 80% of the simulations. The performance of MFO-ELM is more competitive, and it is recommended for solving classification problems.

This paper evaluates the error measures of missing value imputations in medical research. Several imputation techniques have been designed and implemented, however, the evaluation of the degree of deviation of the imputed values from the original values have not been given adequate attention. Predictive Mean Matching Imputation (PMMI) and K-Nearest Neighbour Imputation (KNNI) techniques were implemented on imputation of fertility dataset. The implementation was on three mechanisms of missing values: Missing At Random (MAR), Missing Completely At Random (MCAR) and Missing Not At Random (MNAR). The results were evaluated by mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). PMMI performed better than KNNI in all the results. MSE for example, has the ratio of 0.0260/2.8555 (PMMI/KNNI) for 1-10% MAR – 99.09% reduced error rate; 0.1108/3.0120 (PMMI/KNNI) for 30-40% MCAR – 96.32 reduced error rate; and 0.0642/3.7187 (PMMI/KNNI) for 40-50% MNAR – 98.27% reduced error rate. MCAR was the most consistent missingness mechanism for the evaluations. Density distributions of the imputed dataset were compared with the original dataset. The distribution plots of the imputed missing data followed the curve of the original dataset.

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