We investigate the idea of using diversified multiple trees for Microarray data classification. We propose an algorithm of Maximally Diversified Multiple Trees (MDMT), which makes use of a set of unique trees in the decision committee. We compare MDMT with some well-known ensemble methods, namely AdaBoost, Bagging, and Random Forests. We also compare MDMT with a diversified decision tree algori...

In machine learning, ensembles of models based on Multi-Layer Perceptrons (MLPs) or decision trees are considered successful models. However, explaining their responses is a complex problem that requires the creation new methods interpretation. A natural way to explain classifications transform them into propositional rules. this work, we focus random forests and gradient-boosted trees. Specifi...

Random forests are known to be good for data mining of classification tasks, because random forests are robust for datasets having insufficient information possibly with some errors. But applying random forests blindly may not produce good results, and a dataset in the domain of rotogravure printing is one of such datasets. Hence, in this paper, some best classification accuracy based on clever...

We propose a novel algorithm for optimizing multivariate linear threshold functions as split functions of decision trees to create improved Random Forest classifiers. Standard tree induction methods resort to sampling and exhaustive search to find good univariate split functions. In contrast, our method computes a linear combination of the features at each node, and optimizes the parameters of ...

In Random Forests [2] several trees are constructed from bootstrapor subsamples of the original data. Random Forests have become very popular, e.g., in the fields of genetics and bioinformatics, because they can deal with high-dimensional problems including complex interaction effects. Conditional Inference Forests [8] provide an implementation of Random Forests with unbiased variable selection...

Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are ...

We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than boosted trees, random forests and logistic reg...