Zoran Bosnic

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The paper compares different approaches to estimate the reliability of individual predictions in regression. We compare the sensitivity-based reliability estimates developed in our previous work with four approaches found in the literature: variance of bagged models, local cross-validation, density estimation, and local modeling. By combining pairs of(More)
This paper proposes a system for the early automatic recognition of health problems that manifest themselves in distinctive form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. When the system identifies a health problem, it automatically notifies a physician and provides an explanation of the automatic diagnosis.(More)
In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers’ predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation(More)
Classification and regression models, either automatically generated from data by machine learning algorithms, or manually encoded with the help of domain experts, are daily used to predict the labels of new instances. Each such individual prediction, in order to be accepted/trusted by users, should be accompanied by an explanation of the prediction as well(More)
The use of ROC (Receiver Operating Characteristics) analysis as a tool for evaluating the performance of classification models in machine learning has been increasing in the last decade. Among the most notable advances in this area are the extension of two-class ROC analysis to the multi-class case as well as the employment of ROC analysis in cost-sensitive(More)
For a given prediction model, some predictions may be reliable while others may be unreliable. The average accuracy of the system cannot provide the reliability estimate for a single particular prediction. The measure of individual prediction reliability can be important information in risk-sensitive applications of machine learning (e.g. medicine,(More)
In machine learning community there are many efforts to improve overall reliability of predictors measured as an error on the testing set. But in contrast, very little research has been done concerning prediction reliability of a single answer. This article describes an algorithm that can be used for evaluation of prediction reliability in regression. The(More)
In Machine Learning, estimation of the predictive accuracy for a given model is most commonly approached by analyzing the average accuracy of the model. In general, the predictive models do not provide accuracy estimates for their individual predictions. The reliability estimates of individual predictions require the analysis of various model and instance(More)
In the paper, we present an empirical evaluation of five feature selection methods: ReliefF, random forest feature selector, sequential forward selection, sequential backward selection, and Gini index. Among the evaluated methods, the random forest feature selector has not yet been widely compared to the other methods. In our evaluation, we test how the(More)