TY - GEN
T1 - A novel multiple classifier generation and combination framework based on fuzzy clustering and individualized ensemble construction
AU - Gao, Zhen
AU - Zand, Maryam
AU - Ruan, Jianhua
N1 - Funding Information:
This research was supported in part by grants from the National Science Foundation (award number IIS-1218201 and ABI-1565076), and the National Institutes of Health (award number G12MD007591 and U54CA217297).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MCS - base classifier generation and multiple classifier combination, need to be designed coordinately to produce robust results. In this work, we show that for different testing instances, better classifiers may be trained from different subdomains of training instances including, for example, neighboring instances of the testing instance, or even instances far away from the testing instance. To utilize this intuition, we propose Individualized Classifier Ensemble (ICE). ICE groups training data into overlapping clusters, builds a classifier for each cluster, and then associates each training instance to the top-performing models while taking into account model types and frequency. In testing, ICE finds the k most similar training instances for a testing instance, then predicts class label of the testing instance by averaging the prediction from models associated with these training instances. Evaluation results on 49 benchmarks show that ICE has a stable improvement on a significant proportion of datasets over existing MCS methods. ICE provides a novel choice of utilizing internal patterns among instances to improve classification, and can be easily combined with various classification models and applied to many application domains.
AB - Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MCS - base classifier generation and multiple classifier combination, need to be designed coordinately to produce robust results. In this work, we show that for different testing instances, better classifiers may be trained from different subdomains of training instances including, for example, neighboring instances of the testing instance, or even instances far away from the testing instance. To utilize this intuition, we propose Individualized Classifier Ensemble (ICE). ICE groups training data into overlapping clusters, builds a classifier for each cluster, and then associates each training instance to the top-performing models while taking into account model types and frequency. In testing, ICE finds the k most similar training instances for a testing instance, then predicts class label of the testing instance by averaging the prediction from models associated with these training instances. Evaluation results on 49 benchmarks show that ICE has a stable improvement on a significant proportion of datasets over existing MCS methods. ICE provides a novel choice of utilizing internal patterns among instances to improve classification, and can be easily combined with various classification models and applied to many application domains.
KW - Classification
KW - Ensemble Learning
KW - Instance Selection
KW - Model Selection
KW - Multiple classifier system
UR - http://www.scopus.com/inward/record.url?scp=85079285200&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079285200&partnerID=8YFLogxK
U2 - 10.1109/DSAA.2019.00038
DO - 10.1109/DSAA.2019.00038
M3 - Conference contribution
AN - SCOPUS:85079285200
T3 - Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
SP - 231
EP - 240
BT - Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
A2 - Singh, Lisa
A2 - De Veaux, Richard
A2 - Karypis, George
A2 - Bonchi, Francesco
A2 - Hill, Jennifer
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
Y2 - 5 October 2019 through 8 October 2019
ER -