Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems
| Autor: | Adam Dunlop, Edward Kim, Ethan Jacob Moyer, Isamu Mclean Isozaki, Daniel M. Schwartz, Yigit Alparslan, Shesh Dave |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Binary search algorithm Artificial neural network Computer Science - Artificial Intelligence Computer science business.industry Deep learning Machine learning computer.software_genre Machine Learning (cs.LG) Statistical classification Artificial Intelligence (cs.AI) Binary classification Search algorithm Pattern recognition (psychology) Artificial intelligence business computer Linear search |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn52387.2021.9533483 |
| Popis: | In recent years, deep neural networks have had great success in machine learning and pattern recognition. Architecture size for a neural network contributes significantly to the success of any neural network. In this study, we optimize the selection process by investigating different search algorithms to find a neural network architecture size that yields the highest accuracy. We apply binary search on a very well-defined binary classification network search space and compare the results to those of linear search. We also propose how to relax some of the assumptions regarding the dataset so that our solution can be generalized to any binary classification problem. We report a 100-fold running time improvement over the naive linear search when we apply the binary search method to our datasets in order to find the best architecture candidate. By finding the optimal architecture size for any binary classification problem quickly, we hope that our research contributes to discovering intelligent algorithms for optimizing architecture size selection in machine learning. Comment: 8 pages, 11 figures |
| Databáze: | OpenAIRE |
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