We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the ensemble predictions on unlabeled data to yield significant performance improvements. It does this without making assumptions on the structure or origin of the ensemble, without parameters, and as scalably as linear lea...

Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...

Nahla A Belal A Random Forest Model for Mental Disorders Diagnostic Systems Data mining has established new applications in medicine over the last few years. Using mental disorders diagnostic systems, data possession, and data analysis has been of enormous succor for clinicians to recognize diseases more precisely, especially when dealing with overlapping mental symptoms. In this study, random ...

In this study we investigate the correlation between student behavior and performance in an online course. We introduce highlevel behavior features derived from the course syllabus and sequential patterns. We propose a random forest algorithm with cross-validation to find correlation between behavior features and student performance. Considering a course with 10 periods, our empirical results i...

For the third consecutive year, a SAT competition was organized as a joint event with the SAT conference. With 55 solvers from 25 author groups, the competition was a clear success. One of the noticeable facts from the 2004 competition is the superiority of incomplete solvers on satisfiable random k-SAT benchmarks. It can also be pointed out that the complete solvers awarded this year, namely Z...

We present a fully automatic arm and hand tracker that detects joint positions over continuous sign language video sequences of more than an hour in length. Our framework replicates the state-of-the-art long term tracker by Buehler et al. (IJCV 2011), but does not require the manual annotation and, after automatic initialisation, performs tracking in real-time. We cast the problem as a generic ...

Human action recognition is an important yet challenging task. In this paper, a simple and efficient method based on random forests is proposed for human action recognition. First, we extract the 3D skeletal joint locations from depth images. The APJ3D computed from the action depth image sequences by employing the 3D joint position features and the 3D joint angle features, and then clustered i...

This paper introduces a new general framework for forest-type regression which allows the development of robust forest regressors by selecting from a large family of robust loss functions. In particular, when plugged in the squared error and quantile losses, it will recover the classical random forest (Breiman, 2001) and quantile random forest (Meinshausen, 2006). We then use robust loss functi...

We study the feasibility and time of communication in random geometric radio networks, where nodes fail randomly with positive correlation. We consider a set of radio stations with the same communication range, distributed in a random uniform way on a unit square region. In order to capture fault dependencies, we introduce the ranged spot model in which damaging events, called spots, occur rand...