Random Forest is an ensemble learning method used for classification and regression. In such an ensemble, multiple classifiers are used where each classifier casts one vote for its predicted class label. Majority voting is then used to determine the class label for unlabelled instances. Since it has been proven empirically that ensembles tend to yield better results when there is a significant ...

In this study, an efficient and robust method classifying the minute based occurrence of sleep apnea is aimed. Three respiration signals obtained from abdominal, chest and nasal way extracted from polysomnography recordings. Wavelet transform based on feature extraction methods are applied on the 1 minute length respiration signals. Dimension reduction process is facilitated by using principal ...

The accuracy of supervised land cover classifications depends on several factors like the chosen algorithm, adequate training data and the selection of features. In regard to multi-temporal remote sensing imagery statistical classifier are often not applicable. In the study presented here, a Random Forest was applied to a SAR data set, consisting of 15 acquisitions. A detailed accuracy assessme...

Deep Learning is considered to be a quite young in the area of machine learning research, found its effectiveness in dealing complex yet high dimensional dataset that includes but limited to: images, text and speech etc. with multiple levels of representation and abstraction. As there are plethora of research on these datasets by various researchers , a win over them needs a lots of attention. ...

The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures....

CRISPRs (clustered regularly interspaced short palindromic repeats) are particular repeat sequences found in wide range of bacteria and archaea genomes. Several tools are available for detecting CRISPR arrays in the genomes of both domains. Here we developed a new web-based CRISPR detection tool named CRF (CRISPR Finder by Random Forest). Different from other CRISPR detection tools, a random fo...

Statistical classification of hyperspectral data is challenging because the input space is high in dimension and correlated, but labeled information to characterize the class distributions is typically sparse. The resulting classifiers are often unstable and have poor generalization. A new approach that is based on the concept of random forests of classifiers and implemented within a multiclass...

Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel pr...

In this paper, we propose a classifier for predicting message-level sentiments of English micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Severyn and Moschitti, 2015a; Severyn and Moschitti, 2015b). We leverage large amounts of data with distant supervision to train an ensemble of 2-layer convolutional neural networks whose pre...

Ensemble methods are often used to decide on a good selection of features for later processing by a classifier. Examples of this are in the determination of Random Forest variable importance proposed by Breiman, and in the concept of feature selection ensembles, where the outputs of multiple feature selectors are combined to yield more robust results. All of these methods rely critically on the...