[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

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