Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems
This work addresses the need for faster architecture optimization in binary classification problems, though it is incremental as it adapts existing search methods.
The study tackled the problem of efficiently selecting neural network architecture sizes for binary classification by comparing binary search to linear search, achieving a 100-fold improvement in running time.
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.