Information Sciences: an International Journal
Prediction games and arcing algorithms
Neural Computation
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Computers and Operations Research
RBF-based neurodynamic nearest neighbor classification in real pattern space
Pattern Recognition
Constructing of the risk classification model of cervical cancer by artificial neural network
Expert Systems with Applications: An International Journal
Optimizing feedforward artificial neural network architecture
Engineering Applications of Artificial Intelligence
Comparison of four approaches to a rock facies classification problem
Computers & Geosciences
A hybrid classification method using error pattern modeling
Expert Systems with Applications: An International Journal
Atrial fibrillation classification with artificial neural networks
Pattern Recognition
Temporal gene expression classification with regularised neural network
International Journal of Bioinformatics Research and Applications
Computational Biology and Chemistry
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
Particle swarm optimized multiple regression linear model for data classification
Applied Soft Computing
Automatic classification of Tamil documents using vector space model and artificial neural network
Expert Systems with Applications: An International Journal
Evolutionary Artificial Neural Network Design and Training for wood veneer classification
Engineering Applications of Artificial Intelligence
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Classıfıcation of sleep apnea by using wavelet transform and artificial neural networks
Expert Systems with Applications: An International Journal
On optimum choice of k in nearest neighbor classification
Computational Statistics & Data Analysis
Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems
Expert Systems with Applications: An International Journal
Computers and Operations Research
Computers in Biology and Medicine
A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm
Expert Systems with Applications: An International Journal
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
An adaptive classification system for video-based face recognition
Information Sciences: an International Journal
DSP-based hierarchical neural network modulation signal classification
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The classification problem of assigning several observations into different disjoint groups plays an important role in business decision making and many other areas. Developing more accurate and widely applicable classification models has significant implications in these areas. It is the reason that despite of the numerous classification models available, the research for improving the effectiveness of these models has never stopped. Combining several models or using hybrid models has become a common practice in order to overcome the deficiencies of single models and can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. In this paper, a novel hybridization of artificial neural networks (ANNs) is proposed using multiple linear regression models in order to yield more general and more accurate model than traditional artificial neural networks for solving classification problems. Empirical results indicate that the proposed hybrid model exhibits effectively improved classification accuracy in comparison with traditional artificial neural networks and also some other classification models such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), K-nearest neighbor (KNN), and support vector machines (SVMs) using benchmark and real-world application data sets. These data sets vary in the number of classes (two versus multiple) and the source of the data (synthetic versus real-world). Therefore, it can be applied as an appropriate alternate approach for solving classification problems, specifically when higher forecasting accuracy is needed.