Learning from the Past: Fast NAS for Tasks and Datasets

Abstract

Nowadays, with the advancement of technology, many retail companies require in-house data scientist teams to build machine learning tasks, such as user segmentation and item price prediction. These teams typically use a trial-and-error process to obtain a good model for a given dataset and machine learning task, which is time-consuming and requires expertise. However, the team may have built models for other tasks on different datasets. This article proposes a framework to obtain a model architecture using the previous solved machine learning tasks and datasets. By analyzing real datasets with over 70,000 images from 11 online retail e-commerce websites, it is demonstrated that the performance of a model is related to the similarity among datasets, models, and machine learning tasks. A framework is hence proposed to obtain the model using the similarities among them. It was proven that the model was 26.6% better in accuracy, and using only 20% of the runtime while comparing to an auto network architecture search library, Auto-Keras, in predicting the attributes of fashion images. To the best of our knowledge, this is the first article to obtain the best model based on the similarity among machine learning tasks, models, and datasets.