A Heuristic for Efficient Reduction in Hidden Layer Combinations For Feedforward Neural Networks
This addresses the computational inefficiency of hyper-parameter tuning for machine learning practitioners, but it is incremental as it builds on existing heuristic methods.
The paper tackles the hyper-parameter search problem in machine learning by proposing a heuristic approach, showing a significant reduction in time to obtain models with marginal differences in evaluation metrics compared to exhaustive search.
In this paper, we describe the hyper-parameter search problem in the field of machine learning and present a heuristic approach in an attempt to tackle it. In most learning algorithms, a set of hyper-parameters must be determined before training commences. The choice of hyper-parameters can affect the final model's performance significantly, but yet determining a good choice of hyper-parameters is in most cases complex and consumes large amount of computing resources. In this paper, we show the differences between an exhaustive search of hyper-parameters and a heuristic search, and show that there is a significant reduction in time taken to obtain the resulting model with marginal differences in evaluation metrics when compared to the benchmark case.