Decision trees and ensembles of decision trees are very popular in machine learning and often achieve state-of-the-art performance on black-box prediction tasks. However, popular variants such as C4.5, CART, boosted trees and random forests lack a probabilistic interpretation since they usually just specify an algorithm for training a model. We take a probabilistic approach where we cast the de...

In this paper we propose an automated method for assessing motor symptoms in Parkinson’s disease. Levodopa-induced dyskinesia (LID) and Freezing of Gait (FoG) are detected based on the analysis of signals recorded from wearable devices, i.e. accelerometers and gyroscopes, which are placed on certain positions on the patient’s body. The signals are initially pre-processed and then analyzed, usin...

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...

Patient length of stay (LOS) is a vital metric for hospital operational efficiency, and shorter LOS tied to better patient outcomes improved financial performance. Models that provide accurate, real-time forecasts can help hospitals effectively manage their resources bed capacity. Forecasting perfect problem modern machine learning methods. In this paper, we conduct descriptive literature revie...

Land cover mapping for large regions often employs satellite images of medium to coarse spatial resolution, which complicates mapping of discrete classes. Class memberships, which estimate the proportion of each class for every pixel, have been suggested as an alternative. This paper compares different strategies of training data allocation for discrete and continuous land cover mapping using c...

difference aspects of multinomial statistical modelings and its classifications has been studied so far. in these type of problems y is the qualitative random variable with t possible states which are considered as classifications. the goal is prediction of y based on a random vector x ? ir^m. many methods for analyzing these problems were considered. one of the modern and general method of cla...