Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This stud...

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is first spaceborne rotating fan-beam (RFSCAT). A new algorithm for classification of Arctic sea ice types on measurement data using a random forest classifier presented. trained National Snow and Ice Data Center (NSIDC) weekly age concentration product. Five feature parameters, including mean valu...

We present our approach to predicting the severity of user posts in a mental health forum. This system was developed to compete in the 2016 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. Our entry employs a meta-classifier which uses a set of of base classifiers constructed from lexical, syntactic and metadata features. These classifiers were generated for both the tar...

In the machine learning literature many supervised algorithms have been proposed to perform pattern classification tasks. But in many pattern recognition tasks, labels are often expensive to obtain while a vast amount of unlabeled data are easily available. And redundant samples are often included in the training set, thus slowing down the training process of the classifier without improving cl...

In this paper we study supervised and semi-supervised classification of e-mails. We consider two tasks: filing e-mails into folders and spam e-mail filtering. Firstly, in a supervised learning setting, we investigate the use of random forest for automatic e-mail filing into folders and spam e-mail filtering. We show that random forest is a good choice for these tasks as it runs fast on large an...

Random Forest algorithm have been successfully used in many computer vision tasks such as image classification [1] and image segmentation [4]. Recently, Yao et al. showed that a random forest composed of the decision trees where every node is a discriminative classifier outperforms state-of-the-art results in the finegrained image categorization problems [8]. Yao et al. attributed their success...