Self-Confidence Measures of a Decision Support System Based on Bayesian Networks

A prominent formalism used in decision support is decision theory, which relies on probability theory to model uncertainty about unknown information. A decision support system relying on this theory produces conditional probability as a response. The quality of a decision support system's response depends on three key factors: the amount of data available to train the model, the amount of information about the case at hand, and the adequacy of the system's model to the case at hand. In this dissertation, I investigate different approaches to measuring the confidence of decision support systems based on Bayesian networks addressing the three key factors mentioned above. Some of such confidence measures of the system response have been already proposed. I propose and discuss other measures based on analysis of joint probability distribution encoded by a Bayesian network. The main contribution of this dissertation is the analysis of the discussed measures whether they provide useful information about the performance of a Bayesian network model. I start the analysis with an investigation of interactions among these measures. Then, I investigate whether confidence measures help us predict an erroneous response of a classifier based on Bayesian networks when applied to a particular case. The results suggest that the discussed measures may be considered as indicators for possible mistakes in classification. Further, I conduct an experiment to check how confidence measures perform in combining the models' output in the ensemble of classifiers by weighting. Based on the findings presented in this dissertation, I conclude that the confidence measures may enrich the decision support system's output to serve as indicators for applicability of the model and its advice to a given case

Parameterization of the Individual Tree Detection Method Using Large Dataset from Ground Sample Plots and Airborne Laser Scanning for Stands Inventory in Coniferous Forest

Highly accurate and extensive datasets are needed for the practical implementation of precision forestry as a method of forest ecosystem management. Proper processing of huge datasets involves the necessity of the appropriate selection of methods for their analysis and optimization. In this paper, we propose a concept for and implementation of a data preprocessing algorithm, and a method for the empirical verification of selected individual tree detection (ITD) algorithms, based on Airborne Laser Scanning (ALS) data. In our study, we used ALS data and very extensive dendrometric field measurements (including over 21,000 trees on 522 circular sample plots) in the economic and protective coniferous stands of north-eastern Poland. Our algorithm deals well with the overestimation problems of tree top detection. Furthermore, we analyzed segmentation parameters for the two currently dominant ITD methods: Watershed (WS) and Local Maximum Filter with Growing Region (LMF+GR). We optimized them with respect to minimizing the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Additionally, our results show the crucial importance of the quality of empirical data for the correct evaluation of the accuracy of ITD algorithms