시계열 데이터의 자동기계학습을 위한 메타 모델 기반의 특징 선택 방법

Abstract

Feature engineering is a key step to construct machine learning model as it determines the upper limit of model’s performance. However, designing feature engineering is generally iterative, complex and time-consuming step. Also, the large scale of time series data is rapidly generated from the industry, but there is a shortage of data scientists to handle them. So, it has become necessary to automate this process. In this paper, we aim to develop a meta model-based feature selection method that can learn about which features work best given the dataset. The proposed meta-model is a kind of warm-start that can search from the candidate features that is expected to be good without starting a new search for each data. Proposed method is compared by real time-series datasets obtained from UEA & UCR Time Series Classification Repository. Then, we show the proposed method outperforms random search in terms of F1-measure.