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...

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...

the needs of Internet applications QoS guarantee increased the demand of internet traffic classification, especially for interactive real time applications. Therefore, several classification methods were developed. Machine Learning (ML) classification is one of the most modern techniques, which solves the problem of traditional port base method. This paper compared experimentally the accuracy o...

The PAN 2016 author profiling task is a supervised classification problem on cross-genre documents (tweets, blog and social media posts). Our system makes use of concreteness, sentiment and syntactic information present in the documents. We train a random forest model to identify gender and age of a document’s author. We report the evaluation results received by the shared task.

The experiments aimed to compare the performance of random subspace and random forest models with bagging ensembles and single models in respect of its predictive accuracy were conducted using two popular algorithms M5 tree and multilayer perceptron. All tests were carried out in the WEKA data mining system within the framework of 10-fold cross-validation and repeated holdout splits. A comprehe...

Ensemble learning techniques and in particular Random Forests have been one of the most successful machine learning approaches of the last decade. Despite their success, there exist barely suitable visualizations of Random Forests, which allow a fast and accurate understanding of how well they perform a certain task and what leads to this performance. This paper proposes an exemplar-driven visu...

The complex numerical climate models pose a big challenge for scientists in weather predictions, especially tropical system. This paper is focused on presenting the importance of prediction using machine learning (ML) technique. Recently many researchers recommended that can produce sensible predictions spite having no precise knowledge atmospheric physics. In this work, global solar radiation ...

Recent development of artificial intelligence (AI) and machine learning system has a potential to improve the clinical diagnosis of coronary artery disease. Coronary computed tomography angiography (CCTA) provides important information of coronary arteries: i.e., stenosis severity, lesion length, plaque attenuation, and degree of calcium deposition. However, the comprehensive analysis of these ...

Heart disease is one of the most common causes death globally. In this study, machine learning algorithms and models widely used in literature to predict heart have been extensively compared, a hybrid feature selection based on genetic algorithm tabu search methods developed. The proposed system consists three components: (1) preprocess datasets, (2) with algorithm, (3) classification module. t...

We investigate machine learning techniques for coping with highly skewed class distributions in two spontaneous speech processing tasks. Both tasks, sentence boundary and disfluency detection, provide important structural information for downstream language processing modules. We examine the effect of data set size, task, sampling method (no sampling, downsampling, oversampling, and ensemble sa...