AccurateML: Information-aggregation-based Approximate Processing for Fast and Accurate Machine Learning on MapReduce

TL;DR

AccurateML introduces an information-aggregation approach for fast, approximate machine learning on MapReduce, significantly reducing execution time while maintaining high accuracy compared to existing methods.

Contribution

It proposes a novel aggregation-based technique that improves approximate processing efficiency and accuracy in large-scale MapReduce machine learning tasks.

Findings

Reduces execution time by 30 times with minimal accuracy loss.

Achieves 2.71 times lower accuracy loss than existing methods at the same runtime.

Effectively identifies data parts most relevant to accuracy improvements.

Abstract

The growing demands of processing massive datasets have promoted irresistible trends of running machine learning applications on MapReduce. When processing large input data, it is often of greater values to produce fast and accurate enough approximate results than slow exact results. Existing techniques produce approximate results by processing parts of the input data, thus incurring large accuracy losses when using short job execution times, because all the skipped input data potentially contributes to result accuracy. We address this limitation by proposing AccurateML that aggregates information of input data in each map task to create small aggregated data points. These aggregated points enable all map tasks producing initial outputs quickly to save computation times and decrease the outputs' size to reduce communication times. Our approach further identifies the parts of input data most related to result accuracy, thus first using these parts to improve the produced outputs to minimize accuracy losses. We evaluated AccurateML using real machine learning applications and datasets. The results show: (i) it reduces execution times by 30 times with small accuracy losses compared to exact results; (ii) when using the same execution times, it achieves 2.71 times reductions in accuracy losses compared to existing approximate processing techniques.