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Comparison of Classification Methods
Dočekal, Martin ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Feature Selection Based on Combination of Uncorrelated Evaluation Functions
Vaculík, Karel ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
In order to process large amount of data, it is necessary to use computers. It is possible to use statistical methods or machine learning in some cases. In either case, data can be represented with large number of features. Selection of suitable subset of features can be crucial for efficient processing. This thesis explores a subgroup of feature selection methods which are called filter methods. Comparison of such methods is carried out and the results are used in the design of a new method. This new method uses a combination of existing methods.
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Stress detection
Jindra, Jakub ; Vítek, Martin (referee) ; Němcová, Andrea (advisor)
Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.
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Effect of HFS Based Feature Selection on Cluster Analysis
Malásek, Jan ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
Master´s thesis is focused on cluster analysis. Clustering has its roots in many areas, including data mining, statistics, biology and machine learning. The aim of this thesis is to elaborate a recherche of cluster analysis methods, methods for determining number of clusters and a short survey of feature selection methods for unsupervised learning. The very important part of this thesis is software realization for comparing different cluster analysis methods focused on finding optimal number of clusters and sorting data points into correct classes. The program also consists of feature selection HFS method implementation. Experimental methods validation was processed in Matlab environment. The end of master´s thesis compares success of clustering methods using data with known output classes and assesses contribution of feature selection HFS method for unsupervised learning for quality of cluster analysis.
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Comparison of Classification Methods
Dočekal, Martin ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Stress detection
Jindra, Jakub ; Vítek, Martin (referee) ; Němcová, Andrea (advisor)
Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.
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Feature Selection Based on Combination of Uncorrelated Evaluation Functions
Vaculík, Karel ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
In order to process large amount of data, it is necessary to use computers. It is possible to use statistical methods or machine learning in some cases. In either case, data can be represented with large number of features. Selection of suitable subset of features can be crucial for efficient processing. This thesis explores a subgroup of feature selection methods which are called filter methods. Comparison of such methods is carried out and the results are used in the design of a new method. This new method uses a combination of existing methods.
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Effect of HFS Based Feature Selection on Cluster Analysis
Malásek, Jan ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
Master´s thesis is focused on cluster analysis. Clustering has its roots in many areas, including data mining, statistics, biology and machine learning. The aim of this thesis is to elaborate a recherche of cluster analysis methods, methods for determining number of clusters and a short survey of feature selection methods for unsupervised learning. The very important part of this thesis is software realization for comparing different cluster analysis methods focused on finding optimal number of clusters and sorting data points into correct classes. The program also consists of feature selection HFS method implementation. Experimental methods validation was processed in Matlab environment. The end of master´s thesis compares success of clustering methods using data with known output classes and assesses contribution of feature selection HFS method for unsupervised learning for quality of cluster analysis.
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Utilization of advanced statistical methods for processing of florescence emission of plants affected by local biotic stress
MATOUŠ, Karel
Chlorophyll fluorescence imaging is noninvasive technique often used in plant physiology, molecular biology and precision farming. Captured sequences of images record the dynamic of chlorophyll fluorescence emission which contain the information about spatial and time changes of photosynthetic activity of plant. The goal of this Ph.D. thesis is to contribute to the development of chlorophyll fluorescence imaging by application of advanced statistical techniques. Methods of statistical pattern recognition allow to identify images in the captured sequence that are reach for information about observed biotic stress and to find small subsets of fluorescence images suitable for following analysis. I utilized only methods for identification of small sets of images providing high performance with realistic time consumptions.
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