Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems

TL;DR

This paper introduces a binary search method to efficiently identify optimal neural network architecture sizes for binary classification, significantly reducing search time compared to linear methods.

Contribution

It proposes a binary search approach for architecture size optimization in binary classification, with generalization strategies and substantial time savings.

Findings

100-fold reduction in search time

Effective binary search over architecture space

Generalizable to various datasets

Abstract

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.