Chaining Zscore and feature scaling methods to improve neural networks for classification.

Neural networks for classification aim at identifying the class label of new observation based training data containing instances whose category memberships are known. Therefore the data fed into neural networks has to be preprocessed to enhance its quality resulting in promoting the extraction of meaningful insights of data. However, the fact of processing data until you have the required high quality is challenging and time-consuming to manually search for the best method in a sequence of preprocessing independent methods. For feature scaling methods, they consist of scaling the dataset into the same range of data without monitoring data outliers that should eventually occur in the data source. Zscore for outlier's detection suffers from the issue of predefining the parameters. This paper discussed various approaches that are applied to scale features and detect outliers during data pre-processing. Thereafter, the paper proposed the algorithm that combines Zscore as an outlier's detection method with every classical feature scaling method in high-dimensional data. The proposed algorithm has benefits in selecting the optimal subset of methods from a sequence of chained methods, detecting outliers, and removing zero variance predictors. The study findings from five sample sizes revealed that the proposed method significantly excels the classical method in terms of accuracy. The outstanding from them was performed at the rate of 99.67% and had a significant difference of 0.20% over classical feature scaling. • Chaining Zscore and feature scaling methods impacts positively classification accuracy. • Chaining Zscore and feature scaling methods selects the optimal sequence of methods. • No feature scaling method outperforms in all machine learning algorithms. • Data Outliers removal from dataset may or may not improve the model accuracy. [ABSTRACT FROM AUTHOR]