Activity Recognition is a typical classification problem. The goal is to detect and recognize everyday activities of a person. This paper presents our approach to measurements and classification of a person’s movements. This is done by using two 3-axis radio accelerometers attached to the person’s body and by reconstruction and interpretation of the user’s behavior. We compared two machine lear...

Degenerative changes to the cervical spine can be accompanied by neck pain, which can result from narrowing of the intervertebral disc space and growth of osteophytes. In a lateral x-ray image of the cervical spine, degenerative changes are characterized by vertebral bodies that have indistinct boundaries and limited spacing between vertebrae. In this paper, we present a machine learning approa...

Classification is an active topic of Machine Learning. The most recent achievements in this domain suggest using ensembles of learners instead of a single classifier to improve classification accuracy. Comparisons between Bagging and Boosting show that classifier ensembles perform better when their members exhibit diversity, that is commit different errors. This paper proposes a genetic algorit...

The diversity of an ensemble of classifiers is known to be an important factor in determining its generalization error. We present a new method for generating ensembles, Decorate (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), that directly constructs diverse hypotheses using additional artificially-constructed training examples. The technique is a simple...

The classification problem is one of the important research subjects in the field of machine learning. However, most machine learning algorithms train a classifier based on the assumption that the number of training examples of classes is almost equal. When a classifier was trained on imbalanced data, the performance of the classifier declined clearly. For resolving the class-imbalanced problem...

The importance of learning distance functions is gradually being acknowledged by the machine learning community, and different techniques are suggested that can successfully learn a strong distance function in many various contexts. Nevertheless the studies in the area are still rather fragmentary; they lack systematic analysis and focus on a limited circle of application domains. In this paper...

This article focuses on signal classification for deep-sea acoustic neutrino detection. In the deep sea, the background of transient signals is very diverse. Approaches like matched filtering are not sufficient to distinguish between neutrinolike signals and other transient signals with similar signature, which are forming the acoustic background for neutrino detection in the deep-sea environme...

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...

Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman propo...

Bike sharing systems have been gaining prominence all over the world with more than 500 successful systems being deployed in major cities like New York, Washington, London. With an increasing awareness of the harms of fossil based mean of transportation, problems of traffic congestion in cities and increasing health consciousness in urban areas, citizens are adopting bike sharing systems with z...