Intelligent data classification using optimized fuzzy neural network and improved cuckoo search optimization.

In data mining, classification is one of the most critical steps in predicting the target class. It is performed by an improved model in existing work in which feature selection is performed based on the bat optimization method to increase the classification accuracy. This study uses an enhanced neural network for classification, including intuitive, interpretable correlated-contours fuzzy rules. Further, a practical model is created based on the extraction of fuzzy rules, where data partitioning is performed via a similarity-based directional component. However, the dataset used for experimentation is noisy and incomplete data values. Due to incompleteness, knowledge discovery is obstructed, and the classification results are affected. Here bat provides very slow convergence and easily falls into local optima. To solve this issue, an improved framework is introduced in which missing value imputation is performed by using k means clustering, and then for feature selection, an improved cuckoo search optimization is used. An enhanced classifier based on fuzzy logic and alex net neural network structure (F-ANNS) is used for classification, and hybrid ant colony particle swarm optimization (HASO) is used for optimizing parameters of the alex net neural network classifier. The results show that the proposed work is more effective in precision, recall, accuracy, and f-measure as shown by experimental results. [ABSTRACT FROM AUTHOR]