Search results for: Sadegh Eskandari
Pattern Analysis and Applications > 2019 > 22 > 3 > 949-963
International Journal of Machine Learning and Cybernetics > 2018 > 9 > 4 > 667-676
Neural Computing and Applications > 2015 > 26 > 1 > 213-222
article
All the traditional feature selection methods assume that the entire input feature set is available from the beginning. However, online streaming features (OSF) are integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with time as new features stream in. A critical challenge for online streaming feature selection (OSFS)...
article
Traditional feature selection methods assume that the entire input feature set is available from the beginning. However, streaming features (SF) is an integral part of many real-world applications. In this scenario, the number of training examples is fixed while the number of features grows with time as new features stream in. A critical challenge for streamwise feature selection (SFS) is the unavailability...
article
A big shortcoming of the simple genetic algorithm is that whenever it converges to a local optimum, its performance is continuously deteriorating, especially in the highly nonlinear problems such as traveling salesman problem (TSP). Therefore, some heuristics such as local search are needed to help genetic algorithm (GA) loops escaping such situations. The critical point in such hybridization is the...
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