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This paper presents a systematic evaluation of Neural Network (NN) forclassification of real-world data. In the field of machine learning, it isoften seen that a single parameter that is 'predictive accuracy' is being usedfor evaluating the performance of a classifier model. However, this parametermight not be considered reliable given a dataset with very high level ofskewness. To demonstrate such behavior, seven different types of datasets havebeen used to evaluate a Multilayer Perceptron (MLP) using twelve(12) differentparameters which include micro- and macro-level estimation. In the presentstudy, the most common problem of prediction called 'multiclass' classificationhas been considered. The results that are obtained for different parameters foreach of the dataset could demonstrate interesting findings to support theusability of these set of performance evaluation parameters.

Reliable Evaluation of Neural Network for Multiclass Classification of Real-world Data