Paper Info
Title
Reliable Evaluation of Neural Network for Multiclass Classification of Real-world Data.
Abstract
This paper presents a systematic evaluation of Neural Network (NN) for classification of real-world data. In the field of machine learning, it is often seen that a single parameter that is u0027predictive accuracyu0027 is being used for evaluating the performance of a classifier model. However, this parameter might not be considered reliable given a dataset with very high level of skewness. To demonstrate such behavior, seven different types of datasets have been used to evaluate a Multilayer Perceptron (MLP) using twelve(12) different parameters which include micro- and macro-level estimation. In the present study, the most common problem of prediction called u0027multiclassu0027 classification has been considered. The results that are obtained for different parameters for each of the dataset could demonstrate interesting findings to support the usability of these set of performance evaluation parameters.
Year
Venue
Field
2016
arXiv: Neural and Evolutionary Computing
Skewness,Pattern recognition,Computer science,Usability,Multilayer perceptron,Artificial intelligence,Artificial neural network,Classifier (linguistics),Machine learning,Multiclass classification
DocType
Volume
Citations 
Journal
abs/1612.00671
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Siddharth Dinesh101.35
Tirtharaj Dash23710.89