TY - GEN
T1 - Hybrid Rule-Based Classification by Integrating Expert Knowledge and Data
AU - Jiao, Lianmeng
AU - Ma, Haonan
AU - Pan, Quan
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The common methods for dealing with classification problems include data-driven models and knowledge-driven models. Recently, some methods were proposed to combine the data-driven model with the knowledge-driven model to construct a hybrid model, which improves the classification performance by complementing each other. However, most of the existing methods just assume that the expert knowledge is known in advance, and do not indicate how to obtain it. To this end, this paper proposes a way to obtain knowledge from experts represented by rules through active learning. Then, a hybrid rule-based classification model is developed by integrating the knowledge-driven rule base and the rule base learned from the training data using genetic algorithm. Experiments based on real datasets demonstrate the superiority of the proposed classification model.
AB - The common methods for dealing with classification problems include data-driven models and knowledge-driven models. Recently, some methods were proposed to combine the data-driven model with the knowledge-driven model to construct a hybrid model, which improves the classification performance by complementing each other. However, most of the existing methods just assume that the expert knowledge is known in advance, and do not indicate how to obtain it. To this end, this paper proposes a way to obtain knowledge from experts represented by rules through active learning. Then, a hybrid rule-based classification model is developed by integrating the knowledge-driven rule base and the rule base learned from the training data using genetic algorithm. Experiments based on real datasets demonstrate the superiority of the proposed classification model.
KW - Active learning
KW - Expert knowledge acquisition
KW - Hybrid classification
UR - https://www.scopus.com/pages/publications/85126538815
U2 - 10.1007/978-3-030-98018-4_17
DO - 10.1007/978-3-030-98018-4_17
M3 - 会议稿件
AN - SCOPUS:85126538815
SN - 9783030980177
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 204
EP - 215
BT - Integrated Uncertainty in Knowledge Modelling and Decision Making - 9th International Symposium, IUKM 2022, Proceedings
A2 - Honda, Katsuhiro
A2 - Entani, Tomoe
A2 - Ubukata, Seiki
A2 - Huynh, Van-Nam
A2 - Inuiguchi, Masahiro
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2022
Y2 - 18 March 2022 through 19 March 2022
ER -