In the second step (feature extraction), the features of columnar objects are determined by four methods, edge line detecting method, intercepting method, rectangle locating method and feature statistic method.
Table 2 presents the performance (in terms of average precision) of each machine learning method and feature set using 5-fold cross-validation.
For dataset A, the method proposed in this paper can be compared to a type of feature extraction method and feature fusion method, respectively, with results shown in Table 2. From Table 2 it can be observed that, compared with the AR coefficient and wavelet coefficient features, the feature fusion obtains a lower error rate for simultaneous channel and feature selection.
Ranking method and Friedman's statistical test are also performed to rank and compare different combinations of feature extraction methods and feature selection methods which endorse the efficacy of the proposed approach.
Additionally, Guyon and Elisseeff elaborated a wide range of aspects in feature selection including a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods and feature validity assessment methods [ 26].
We have demonstrated that genetic algorithm based multi-task learning (GA-MTL) methods perform better than the heuristic methods and feature selection methods, and that e-GA-MTL performs the best of all the methods considered.
Most of the included studies have a descriptive, exploratory design, use qualitative methods and feature data on views and experiences of patients and healthcare staff.
Besides the differences in machine learning methods and feature selection [ 22– 25], the relative smaller sample size and not multicenter data might be responsible for the controversial conclusions when multiple features were applied to predict defibrillation outcome.
Numerical simulation of surface generation is conducted to demonstrate the effectiveness of the novel machining method and features of the obtained hierarchical structures.
Overall, the existing shadow removal methods can be divided into two categories: model-based method and feature-based method.
