Applying Multiple Complementary Neural Networks to Solve Multiclass Classification Problem
نویسنده
چکیده
In this paper, a multiclass classification problem is solved using multiple complementary neural networks. Two techniques are applied to multiple complementary neural networks which are one-against-all and error correcting output codes. We experiment our proposed techniques using an extremely imbalance data set named glass from the UCI machine learning repository. It is found that the combination between multiple complementary neural networks and error correcting output codes provides better performance when compared to the combination between multiple complementary neural networks and one-against-all. Its performance is also better than using a single k-class neural network, a single k-class complementary neural networks, multiple binary neural networks based on error correcting output codes, and multiple binary neural networks based on one-against-all. Keywords— multiclass classification, feedforward backpropagation neural network, complementary neural networks, error correcting output codes, one-against-all, one-against-one, p-against-q
منابع مشابه
One-Against-All Multiclass Classification Based on Multiple Complementary Neural Networks
In general, there are two ways to deal with one-against-all multiclass neural network classification. The first way is the use of a single k-class neural network trained with multiple outputs. Another way is the use of multiple binary neural networks. This paper focuses on the later way in which multiple complementary neural networks are applied to one-against-all instead of using only multiple...
متن کاملMulticlass Classification using Neural Networks and Interval Neutrosophic Sets
This paper presents a new approach to the problem of multiclass classification. The proposed approach has the capability to provide an assessment of the uncertainty value associated with the results of the prediction. Two feed-forward backpropagation neural networks, each with multiple outputs, are used. One network is used to predict degrees of truth membership and another network is used to p...
متن کاملLithofacies Classification from Well Log Data using Neural Networks, Interval Neutrosophic Sets and Quantification of Uncertainty
This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict...
متن کاملA comparison of methods for multiclass support vector machines
Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computation...
متن کاملCorporate Credit Rating using Multiclass Classification Models with order Information
Corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has been one of the attractive research topics in the literature. In recent years, multiclass classification models such as artificial neural network (ANN) or multiclass support vector machine (MSVM) have become a very appealing machine learning approaches due to their good performance. However, mos...
متن کامل