A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

Sumedh Yadav,Mathis Bode

doi:10.1186/s40537-019-0259-3

Abstract

A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. The presented method consists of two approaches, one for reducing a given training set, and another for partitioning the selected/reduced set. The heuristic targets large datasets, since the primary goal is a significant reduction in training computation run-time without compromising prediction accuracy. Test results show that both approaches significantly speed-up the training task when compared against that of state-of-the-art shrinking heuristics available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy. A network design is also presented for a partitioning based distributed training formulation. Added speed-up in training run-time is observed when compared to that of serial implementation of the approaches.

Highlights

Two decades earlier, some of the most seminal works in machine learning were done on training set selection [1, 2] under the banner of relevance reasoning
Results are presented in two major parts, first with tests on parameter space of the heuristic, and second for gauging performance of the heuristic
Significant improvements are observed with more workers

Summary

Introduction

Some of the most seminal works in machine learning were done on training set selection [1, 2] under the banner of relevance reasoning. When data sources are abundant, it is befitting to separate data based on relevance to the learning task. This has led to a renewed interest in the once famous problem statement of relevance reasoning [5, 6]. Reasoning on relevance to get improved scalability of classification algorithms is currently explored on graphical/network data [7] and learned models [8]

Methods

Results

Conclusion