Machine learning: an artificial intelligence approach volume III
Machine learning: an artificial intelligence approach volume III
Technical note: some properties of splitting criteria
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Aggregating expert predictions in a networked environment
Computers and Operations Research
Machine Learning
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
Computers and Operations Research
Computers and Operations Research
Employee turnover: a neural network solution
Computers and Operations Research
Computers and Operations Research
Evolutionary product unit based neural networks for regression
Neural Networks
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
Computational Statistics & Data Analysis
Review: Expert systems and evolutionary computing for financial investing: A review
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
Hybridization of evolutionary algorithms and local search by means of a clustering method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Improvement of accuracy in a sound synthesis method using Evolutionary Product Unit Networks
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This work presents a new approach for multi-class pattern recognition based on the hybridization of a linear and nonlinear model. We propose multinomial logistic regression where some new covariates are defined by a product unit neural network, where in turn, the nonlinear basis functions are constructed with the product of the inputs raised to arbitrary powers. The application of this methodology involves, first of all, training the coefficients and the basis structure of product unit models using techniques based on artificial neural networks and evolutionary algorithms, followed by the application of multinomial logistic regression to both the new derived features and the original ones. To evaluate the efficacy of our technique we pose a difficult problem, the classification of sheep with respect to their milk production in different lactations, using covariates that only involve the first weeks of lactation. This enables the productive capacity of the animal to be identified more rapidly and leads to a faster selection process in determining the best producers. The results obtained with our approach are compared to other classification methodologies. Although several of these methodologies offer good results, the percentage of cases correctly classified was higher with our approach, which shows how instrumental the potential use of this methodology is for decision making in livestock enterprises, a sector relatively untouched by the technological innovations in business management that have been appearing in the last few years.