Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefree data while some of them are better than others on noisy data. But in reality, ensemble methods that can consistently gain good performance in situations with or without noise are more desirable. In this paper, we pr...

Ensemble learning has gained success in machine with major advantages over other methods. Bagging is a prominent ensemble method that creates subgroups of data, known as bags, are trained by individual methods such decision trees. Random forest example bagging additional features the process. Evolutionary algorithms have been for optimisation problems and also used learning. gradient-free work ...

An impulse noise detection scheme employing machine learning (ML) algorithm in Orthogonal Frequency Division Multiplexing (OFDM) is investigated. Four powerful ML's multi-classifiers (ensemble) algorithms (Boosting (Bos), Bagging (Bag), Stacking (Stack) and Random Forest (RF)) were used at the receiver side of the OFDM system to detect if the received noisy signal contained impulse noise or not...

Land cover mapping provides basic information for advanced science such as ecological management, biodiversity conservation, forest planning and so on. In remote sensing research, the process of creating an accurate land cover map is an important subject. Recently, there has been growing research interest in the object-oriented image classification techniques. The object-oriented image classifi...

Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method b...

We describe the approach that we submitted to the 2015 PAN competition [7] for the author identification task. The task consists in determining if an unknown document was authored by the same author of a set of documents with the same author. We propose a machine learning approach based on a number of different features that characterize documents from widely different points of view. We constr...

BACKGROUND We present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. METHODS We employed the National Inpatie...

Environmental audio classification has been the focus in the field of speech recognition. For Environmental audio data, it is difficult to find an optimal classifier and select the optimal features from various features can be extracted. Random forest is a powerful machine learning classifier compared to other conventional pattern recognition techniques. In this paper, the performance of the Ra...

In this article, we discuss how to use a variety of machine learning methods, e.g. tree bagging, random forest, boost, support vector machine, and Gaussian mixture model, for building classifiers for electroencephalogram (EEG) data, which is collected from different brain states on different subjects. Also, we discuss how training data size influences misclassification rate. Moreover, the numbe...

The diagnosis of cancer type based on microarray data offers hope that cancer classification can be highly accurate for clinicians to choose the most appropriate forms of treatment with it. Due to several inherent characteristics associated with microarray data, accurate diagnosis has been an active research topic attracting tremendous research interests in machine learning community. In this p...