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Segmentation of MR images using machine learning algorithms
Dorazil, Jan ; Mikulka, Jan (referee) ; Dvořák, Pavel (advisor)
This thesis concerns with magnetic resonance image segmentation using Random Forests algorithm. Employed technologies accomplishing the specified task include C++ progra- mming language with libraries ITK and OpenCV. This work descibes the technique of processing images from loading through preprocessing to the actual segmentation. The outcome from this work is a programme that automatically segmentates MR images of mouse’s head to the brain and the surroundings.
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Implementation of Algorithms Based on Decision Trees in C#
Grolig, Lukáš ; Pešek, Martin (referee) ; Stríž, Rostislav (advisor)
This bachelor thesis is focused on selection of data mining algorithms based on decision trees for an analytical system developed under the project System for the Internet security increase based on malware spreading analysis. Selected algorithms are described in greater detais, as well as their implementation in the C# language. These algorithms are then tested with regards to their training speed and classification accuracy. Finally, this thesis presents further conclusions and recommendations based on performed experiments.
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Object Detection on GPU
Jurák, Martin ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
This thesis is focused on the acceleration of Random Forest object detection in an image. Random Forest detector is an ensemble of independently evaluated random decision trees. This feature can be used to acceleration on graphics unit. Development and increasing performance of graphics processing units allow the use of GPU for general-purpose computing (GPGPU). The goal of this thesis is describe how to implement Random Forest method on GPU with OpenCL standard.
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Data Mining and use of decision trees by creation of Scorecards
Straková, Kristýna ; Witzany, Jiří (advisor) ; Fičura, Milan (referee)
The thesis presents a comparison of several selected modeling methods used by financial institutions for (not exclusively) decision-making processes. First theoretical part describes well known modeling methods such as logistic regression, decision trees, neural networks, alternating decision trees and relatively new method called "Random forest". The practical part of thesis outlines some processes within financial institutions, in which selected modeling methods are used. On real data of two financial institutions logistic regression, decision trees and decision forest are compared which each other. Method of neural network is not included due to its complex interpretability. In conclusion, based on resulting models, thesis is trying to answers, whether logistic regression (method most widely used by financial institutions) remains most suitable.
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Explaining Anomalies with Sapling Random Forests
Pevný, T. ; Kopp, Martin
The main objective of anomaly detection algorithms is finding samples deviating from the majority. Although a vast number of algorithms designed for this already exist, almost none of them explain, why a particular sample was labelled as an anomaly. To address this issue, we propose an algorithm called Explainer, which returns the explanation of sample’s differentness in disjunctive normal form (DNF), which is easy to understand by humans. Since Explainer treats anomaly detection algorithms as black-boxes, it can be applied in many domains to simplify investigation of anomalies. The core of Explainer is a set of specifically trained trees, which we call sapling random forests. Since their training is fast and memory efficient, the whole algorithm is lightweight and applicable to large databases, datastreams, and real-time problems. The correctness of Explainer is demonstrated on a wide range of synthetic and real world datasets.
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Interpreting and Clustering Outliers with Sapling Random Forests
Kopp, Martin ; Pevný, T. ; Holeňa, Martin
The main objective of outlier detection is finding samples considerably deviating from the majority. Such outliers, often referred to as anomalies, are nowadays more and more important, because they help to uncover interesting events within data. Consequently, a considerable amount of statistical and data mining techniques to identify anomalies was proposed in the last few years, but only a few works at least mentioned why some sample was labelled as an anomaly. Therefore, we propose a method based on specifically trained decision trees, called sapling random forest. Our method is able to interpret the output of arbitrary anomaly detector. The explanation is given as a subset of features, in which the sample is most deviating, or as conjunctions of atomic conditions, which can be viewed as antecedents of logical rules easily understandable by humans. To simplify the investigation of suspicious samples even more, we propose two methods of clustering anomalies into groups. Such clusters can be investigated at once saving time and human efforts. The feasibility of our approach is demonstrated on several synthetic and one real world datasets.
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Methods for Multidimensional Event Classification: A Case Study
Bock, R.K. ; Chilingarian, A. ; Gaug, M. ; Hakl, František ; Hengstebeck, T. ; Jiřina, Marcel ; Klaschka, Jan ; Kotrč, Emil ; Savický, Petr ; Towers, S. ; Vaicilius, A. ; Wittek, W.
Fulltext: content.csg -  PDF
Plný tet: v887-03 - PDF
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