Active learning with binary models for real time data labelling

TL;DR

This paper proposes a real-time data labelling strategy using binary models in active learning, reducing costs and time for labelling large datasets in supervised machine learning tasks.

Contribution

It introduces a novel active learning approach with binary models for efficient real-time data labelling, improving labelling contribution rates.

Findings

Balancing enabled improves labelling contribution to 89% and 81.1%.

Without balancing, contribution is 83.47% and 78.71%.

Method reduces labelling costs and time in supervised learning.

Abstract

Machine learning (ML) and Deep Learning (DL) tasks primarily depend on data. Most of the ML and DL applications involve supervised learning which requires labelled data. In the initial phases of ML realm lack of data used to be a problem, now we are in a new era of big data. The supervised ML algorithms require data to be labelled and of good quality. Labelling task requires a large amount of money and time investment. Data labelling require a skilled person who will charge high for this task, consider the case of the medical field or the data is in bulk that requires a lot of people assigned to label it. The amount of data that is well enough for training needs to be known, money and time can not be wasted to label the whole data. This paper mainly aims to propose a strategy that helps in labelling the data along with oracle in real-time. With balancing on model contribution for labelling is 89 and 81.1 for furniture type and intel scene image data sets respectively. Further with balancing being kept off model contribution is found to be 83.47 and 78.71 for furniture type and flower data sets respectively.