插补(统计学)
缺少数据
计算机科学
数据挖掘
知识抽取
卡尔曼滤波器
领域知识
知识转移
学习迁移
人工智能
机器学习
知识管理
作者
Honggui Han,Mengmeng Li,Junfei Qiao,Qing Yang,Yongzhen Peng
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-12-01
卷期号:35 (12): 12649-12662
标识
DOI:10.1109/tkde.2023.3270118
摘要
Missing data imputation is a critical data processing procedure in wastewater treatment process. However, the existing imputation methods cannot stand the missing data with high proportions that frequently happens due to unmaintained instruments or detection failures. Transfer Learning aims to learn much reliable information for the target domain with previous learned knowledge from a source domain, which provides a framework for solving such problem. This paper proposes a filter transfer learning algorithm (FTLA) for missing data imputation with high proportions. First, a knowledge acquisition strategy is developed to extract the source knowledge, including independent knowledge from historical datasets and parallel knowledge in terms of related datasets. The missing data is then interpreted through source knowledge comprehensively. Second, a filter transfer learning algorithm is designed to achieve target knowledge that mimics the tendency of the missing data. This algorithm can avoid serious negative transfer by using the extended Kalman filter to filtrate source knowledge. Third, a knowledge rolling mechanism is established to perform the imputation online with target knowledge, which can maintain the reliable imputation for missing data with high proportions. Finally, several comparative experiments of wastewater data are provided to demonstrate the merits of missing data imputation with FTLA.
科研通智能强力驱动
Strongly Powered by AbleSci AI