Effectiveness of dataset reduction in testing machine learning algorithms
Jaganmohan Chandrasekaran, Huadong Feng, Yu Lei, Raghu N. Kacker, D. Richard Kuhn · 2020
Many machine learning algorithms examine large amounts of data to discover insights from hidden patterns. Testing these algorithms can be expensive and time-consuming. There is a need to speed up the testing process, especially in an agile development process, where testing is frequently performed. One approach is to replace big datasets with smaller datasets produced by random sampling. In this paper, we report a set of experiments that are designed to evaluate the effectiveness of using reduced datasets produced by random sampling for testing machine learning algorithms. In our experiments, we use as subject programs four supervised learning algorithms from the Waikato Environment for Knowledge Analysis (WEKA). We identify five datasets from Kaggle.com to run with the four learning algorithms. For each dataset, we generate reduced datasets of different sizes using two random sampling strategies, i.e., pure random and stratified random sampling. We execute our subject programs with the original and the reduced datasets, and measure test effectiveness using branch and mutation coverage. Our results indicate that in most cases, reduced datasets of even very small sizes can achieve the same or similar coverage achieved by the original dataset. Furthermore, our results indicate that reduced datasets produced by the two sample strategies do not differ significantly, and branch coverage correlates with mutation coverage.