Modeling of missing data prediction: Computational intelligence and optimization algorithms

Four optimization algorithms (genetic algorithm, simulated annealing, particle swarm optimization and random forest) were applied with an MLP based auto associative neural network on two classification datasets and one prediction dataset. This work was undertaken to investigate the effectiveness of using auto associative neural networks and optimization algorithms in missing data prediction and classification tasks. If performed appropriately, computational intelligence and optimization algorithm systems could lead to consistent, accurate and trustworthy predictions and classifications resulting in more adequate decisions. The results reveal GA, SA and PSO to be more efficient when compared to RF in terms of predicting the forest area to be affected by fire. GA, SA, and PSO had the same accuracy of 93.3%, while RF showed 92.99% accuracy. For the classification problems, RF showed 93.66% and 92.11% accuracy on the German credit and Heart disease datasets respectively, outperforming GA, SA and PSO.