[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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264 H. Josiński et al. Fig. 4. Dimensionality reduction after applying MPCA algorithm The reduced data was classified by two statistical WEKA classifiers. It is a Naive Bayes (NB) and the 1-nearest neighbor (1NN) classifier. The classification was repeated twice for both proposed reduction approaches. In case of the "single dataset", 10-fold cross-validation is used to split data into the training and test parts. The classification accuracy is evaluated by percentage of correctly classified gaits out of the test set. The detailed classification results for the "single dataset" approach are shown in Fig. 5. The mean classification accuracy for all considered Q values of 1NN classifier is 42.94% and the best result is 73.75% obtained by Q = 0.19 and Q = 0.20. There is a noticeable downward trend for the increasing Q parameter. More MPCA components undoubtedly contain more individual data, but also more noise. The situation is quite different for Naive Bayes, which has a higher average score: 53.32% Beside the cases of strongly reduced gaits, ranging to Q = 0.48 with 358 MPCA components, it is much more precise. There is a very clear extreme for Q = 0.78 and 9064 attributes, in which the best classification accuracy of 86.25% is obtained. More MPCA components not only do not improve classification, but very strongly decrease it, to the worst performance of 16.25% for Q = 0.99. Fig. 5. 1NN and NB classification results for the single dataset approach
Selection of Individual Gait Features Extracted by MPCA 265 When the training set of MPCA and the supervised classification contain only half the data in case of "train set and test set" approach, the results are much worse as shown in Fig. 6. The performance does not exceed 30% and 43% of classification accuracy for Naive Bayes and 1NN classifiers, respectively. It is much better than random guessing, which gives only 5%, but still very poor. It is probably caused by the weak representativeness of input and reduced spaces because of the small size of the training set. Only two gait instances of each class are insufficient for effective probability estimation of high dimensional continuous spaces, necessary in statistical classification. Fig. 6. Naïve Bayes classification results for continuous and discretized spaces for train set and test set approach To improve the classification we applied supervised discretization of reduced feature spaces, which should make statistical estimation easier. Because calculating distances in discrete spaces seems to be much less accurate than in the continuous ones, which is a crucial step of 1NN, we repeated the tests only for Naive Bayes classifier. In discretization we applied MDL method proposed by Fayyad and Irani [7]. The results shown in Fig. 7 are even better than we expected. Regardless of the poor representativeness of the training set, the maximum accuracy is 92.50% for Q=0.67 and 0.85, which means only three misclassified gaits. We can locate extreme once again. However the influence of noise in high dimensional spaces is weaker, for Q=0.99 accuracy is still greater than 60%. Fig. 7. Naïve Bayes classification results for continuous and discretized spaces for train set and test set approach Because of such promising results obtained by the classification based on discretized spaces for the "train set and test set" approach, we evaluated the influence of discretization on classification performance for the single dataset approach. As
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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8 A. Babiarz et al. a) b) Fig. 5. T
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10 A. Babiarz et al. o o o “micvo
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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18 A. Babiarz et al. a) b) Fig. 11.
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Recognition and Location of Objects
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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