In this work a novel ensemble technique for generating random decision forests is presented. The proposed technique incorporates a Naive Bayes classification model to increase the diversity of the trees in the forest in order to improve the performance in terms of classification accuracy. Experimental results on several benchmark data sets show that the proposed method archives outstanding pred...

Random Forest is an Ensemble Supervised Machine Learning technique. Research work in the area of Random Forest aims at either improving accuracy or improving performance. In this paper we are presenting our research towards improvement in learning time of Random Forest by proposing a new approach called Disjoint Partitioning. In this approach, we are using disjoint partitions of training datase...

OBJECTIVES To develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the 'Random Forest' algorithm. DESIGN Case-control study. PARTICIPANTS 293 eyes of 179 participants with open angle glaucoma (OAG) or suspected OAG. INTERVENTIONS Spectral doma...

Ensemble methods with Random Oracles have been proposed recently (Kuncheva and Rodŕıguez, 2007). A random-oracle classifier consists of a pair of classifiers and a fixed, randomly created oracle that selects between them. Ensembles of random-oracle decision trees were shown to fare better than standard ensembles. In that study, the oracle for a given tree was a random hyperplane at the root of ...

With increasing popularity of web based learning, it is required to design the web layout to reduce cognitive load. Cognitive load theory is widely used to predict the effectiveness of the web based and multimedia learning. The cognitive load induced by instructional and multimedia modes are measured by indirect or subjective methods. Questionnaires are one common form of measuring cognitive lo...

Mass detection from mammogram plays an crucial role as a pre-processing stage for mass segmentation and classification. In this paper, we present a novel approach for detecting masses from mammograms using a cascade of deep learning and random forest classifiers. The deep learning classifier consists of a multi-scale deep belief network classifier that selects regions to be further processed by...

Search interface detection is an essential task for extracting information from the hidden Web. The challenge for this task is that search interface data is represented in high-dimensional and sparse features with many missing values. This paper presents a new multi-classifier ensemble approach to solving this problem. In this approach, we have extended the random forest algorithm with a weight...

Environmental audio classification has been the focus in the field of speech recognition. For Environmental audio data, it is difficult to find an optimal classifier and select the optimal features from various features can be extracted. Random forest is a powerful machine learning classifier compared to other conventional pattern recognition techniques. In this paper, the performance of the Ra...