In this paper we present our work on the Random Forest (RF) family of classification methods. Our goal is to go one step further in the understanding of RF mechanisms by studying the parametrization of the reference algorithm Forest-RI. In this algorithm, a randomization principle is used during the tree induction process, that randomly selects K features at each node, among which the best spli...

Margin distribution is acknowledged as an important factor for improving the generalization performance of classifiers. In this paper, we propose a novel ensemble learning algorithm named Double Rotation Margin Forest (DRMF), that aims to improve the margin distribution of the combined system over the training set. We utilise random rotation to produce diverse base classifiers, and optimize the...

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall f...

Gaming systems like Kinect and XBox always have to tackle the problem of extracting features from video data sets and classifying the body movement. In this study, reasonable features like human joints positions, joints velocities, joint angles and joint angular velocities are extracted. We used several machine learning methods including Naive Bayes, Support Vector Machine and Random Forest to ...

In this paper, we evaluate the performance of machine learningbased methods for detection of phishing sites. In our previous work [1], we attempted to employ a machine learning technique to improve the detection accuracy. Our preliminary evaluation showed the AdaBoost-based detection method can achieve higher detection accuracy than the traditional detection method. Here, we evaluate the perfor...

Underlying fabrics can change the appearance, function and quality of the garment, and also add so much longevity of the garment. Nowadays, with the increasing use of various types of fabrics in the garment industry, their resistance to bagging is of great importance with the aim of determining the effectiveness of textiles under various forces. The current paper investigated the effect of unde...

Our objective is to build machine learning based models that predict audiovisual quality directly from a set of correlated parameters that are extracted from a target quality dataset. We have used the bitstream version of the INRS audiovisual quality dataset that reflects contemporary realtime configurations for video frame rate, video quantization, noise reduction parameters and network packet...

Random Forests are a popular and powerful machine learning technique, with several fast multi-core CPU implementations. Since many other machine learning methods have seen impressive speedups from GPU implementations, applying GPU acceleration to random forests seems like a natural fit. Previous attempts to use GPUs have relied on coarse-grained task parallelism and have yielded inconclusive or...

The motivation for this study was to learn to predict forest fires in Slovenia using different data mining techniques. We used predictive models based on data from a GIS (geographical information system), the weather prediction model Aladin and MODIS satellite data. We examined three different datasets: one only for the Kras region, one for whole Primorska region and one for continental Sloveni...