Feature Generation Based on Knowledge Graph
Li Li, Haolin Yang, Yueming Jiao, Kuo‐Yi Lin · IFAC-PapersOnLine · 2020
With the continuous improvement of the status of machine learning in business, scientific research and even life, feature engineering, an indispensable part of machine learning tasks, has attracted more and more researchers attention. In feature engineering, feature engineering is an important component for generating new effective information. Researches in the past hope to operate available data to acquire new features, it does not import extra information. Therefore, this paper puts forward the feature generation method based on knowledge graph. This paper introduces the basic application method of knowledge graph by using the convenience of obtaining structured data in knowledge graph. This paper constructs campus big data mental health assessment data through questionnaires and campus databases, uses feature generation methods based on knowledge maps to generate features, obtains new data generated by collation, and uses the model to verify; finally, the experimental results show that after optimization The recall rate of the data set in the four integrated learning models has increased to more than 60%, which can provide preliminary guidance for judging the mental health status of students.