-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical …-nearest neighbors or random forest classification. We apply our model to real corn futures data and generate price scenarios via a Monte …

Advanced analytical techniques are gaining popularity in addressing complex classification type decision problems in …

This article describes how classification methods on software defect prediction is widely researched due to the need to …-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly … recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows …

vector machines, boosting, classification and regression trees, and Random Forests. Finally, because the accept …

the tri-gram frequency statistics profiles for a document. Classification is achieved by computing a dissimilarity measure … classification using the Dice measure outperforms classification using the Manhattan measure. …

artificial neural networks, decision trees and support vector machines), we have developed both classification- and regression …-type models in order to assess the predictive abilities of different methodologies (classification versus regression …-based classification) and techniques. In the end, the results showed that the classification-type models predict the game outcomes better …