A thorough discussion of the random forest (RF) algorithm as it relates to a SELDI-TOF proteomics study is presented, with special emphasis on its application for cancer prevention: specifically, what makes it an efficient, yet reliable classifier, and what makes it optimal among the many available approaches. The main body of the paper treats the particulars of how to successfully apply the RF...

Supervised learning requires a training set on which the classifier’s parameters are learnt. An independent test set is used to evaluate its performance. For each test sample the classifier returns a vector that estimates the probability that the sample belongs to any of the classes under consideration, in our case edges and non-edges. We employ the random forest classifier [1], a state-of-the-...

We describe a machine learning approach, a Random Forest (RF) classifier, that is used to automatically compile bilingual dictionaries of technical terms from comparable corpora. We evaluate the RF classifier against a popular term alignment method, namely context vectors, and we report an improvement of the translation accuracy. As an application, we use the automatically extracted dictionary ...

The main aim of this paper is to analyze the method of Principal Component Analysis (PCA) and its performance when applied to face recognition. In previous work [10], they use Euclidian Distance for recognition. Here we use Neural Network and Random Forest Classifier which has much higher recognition accuracy than other methods. This algorithm creates a subspace (face space) where the faces in ...

In this paper we present an experimental study of the performance of three machine learning algorithms applied to the difficult problem of galaxy classification. We use the Naive Bayes classifier, the rule-induction algorithm C4.5 and a recently introduced classifier named random forest (RF). We first employ image processing to standardize the images, eliminating the effects of orientation and ...

The high-dimensional data has a number of uncertain factors, such as sparse features, repeated features and computational complexity. The random forest algorithm is a ensemble classifier method, and composed of numerous weak classifiers. It can overcome a number of practical problems, such as the small sample size, over-learning, nonlinearity, the curse of dimensionality and local minima, and i...

This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. ...

The JMLR study Do we need hundreds of classifiers to solve real world classification problems? benchmarks 179 classifiers in 17 families on 121 data sets from the UCI repository and claims that “the random forest is clearly the best family of classifier”. In this response, we show that the study’s results are biased by the lack of a held-out test set and the exclusion of trials with errors. Fur...

Classification techniques such as Support Vector Machines, K-Nearest Neighbours, Decision Trees, Logistic Regression and Naive Bayes have widely been used in the area of intrusion detection research in the security community. They are predominantly used for behaviour based detection methods (anomaly detection methods). In this paper we exclusively apply the ensemble learning algorithm Random Fo...

Breast cancer (BC) disease is the most common and rapidly spreading across globe. This can be prevented if identified early, this eventually reduces death rate. Machine learning (ML) frequently utilized technology in research. Cancer patients benefit from early detection diagnosis. Using machine approaches, research proposes an improved way of detecting breast cancer. To deal with problem imbal...