TY - GEN
T1 - Deep embedding logistic regression
AU - Cui, Zhicheng
AU - Zhang, Muhan
AU - Chen, Yixin
N1 - Publisher Copyright:
©2018 IEEE
PY - 2018/12/24
Y1 - 2018/12/24
N2 - Logistic regression (LR) is used in many areas due to its simplicity and interpretability. While at the same time, those two properties limit its classification accuracy. Deep neural networks (DNNs), instead, achieve state-of-the-art performance in many domains. However, the nonlinearity and complexity of DNNs make it less interpretable. To balance interpretability and classification performance, we propose a novel nonlinear model, Deep Embedding Logistic Regression (DELR), which augments LR with a nonlinear dimension-wise feature embedding. In DELR, each feature embedding is learned through a deep and narrow neural network and LR is attached to decide feature importance. A compact and yet powerful model, DELR offers great interpretability: it can tell the importance of each input feature, yield meaningful embedding of categorical features, and extract actionable changes, making it attractive for tasks such as market analysis and clinical prediction.
AB - Logistic regression (LR) is used in many areas due to its simplicity and interpretability. While at the same time, those two properties limit its classification accuracy. Deep neural networks (DNNs), instead, achieve state-of-the-art performance in many domains. However, the nonlinearity and complexity of DNNs make it less interpretable. To balance interpretability and classification performance, we propose a novel nonlinear model, Deep Embedding Logistic Regression (DELR), which augments LR with a nonlinear dimension-wise feature embedding. In DELR, each feature embedding is learned through a deep and narrow neural network and LR is attached to decide feature importance. A compact and yet powerful model, DELR offers great interpretability: it can tell the importance of each input feature, yield meaningful embedding of categorical features, and extract actionable changes, making it attractive for tasks such as market analysis and clinical prediction.
KW - Accountability
KW - Actionability
KW - Classification
KW - Interpretability
UR - http://www.scopus.com/inward/record.url?scp=85061350359&partnerID=8YFLogxK
U2 - 10.1109/ICBK.2018.00031
DO - 10.1109/ICBK.2018.00031
M3 - Conference contribution
AN - SCOPUS:85061350359
T3 - Proceedings - 9th IEEE International Conference on Big Knowledge, ICBK 2018
SP - 176
EP - 183
BT - Proceedings - 9th IEEE International Conference on Big Knowledge, ICBK 2018
A2 - Soon, Ong Yew
A2 - Chen, Huanhuan
A2 - Wu, Xindong
A2 - Aggarwal, Charu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Big Knowledge, ICBK 2018
Y2 - 17 November 2018 through 18 November 2018
ER -