TY - GEN
T1 - New 2-Tier Multiclass Prediction Framework
AU - Awad, Mamoun
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/23
Y1 - 2015/7/23
N2 - In multiclass classification problems we face the challenge of having many binary classifiers. Consulting this large number of classifiers might be confusing and time consuming. In this paper, we propose a new framework for training and prediction in multiclass problems. In this framework, we perform traditional training. Next we map training examples to prediction models. Finally we produce the Example Classifier (EC). In prediction a new example is passed through the EC to determine the appropriate classifier which in turn makes the last prediction decision. We conduct experiments comparing our framework with one-VS-one and Directed Acyclic Graph (DAG) using Support Vector Machines. Additionally, we compare our model with well-known ensemble models, namely, AdaBoost and Bagging, Our results indicate that prediction accuracy is comparable to other methodologies with the advantage of consuming less prediction time.
AB - In multiclass classification problems we face the challenge of having many binary classifiers. Consulting this large number of classifiers might be confusing and time consuming. In this paper, we propose a new framework for training and prediction in multiclass problems. In this framework, we perform traditional training. Next we map training examples to prediction models. Finally we produce the Example Classifier (EC). In prediction a new example is passed through the EC to determine the appropriate classifier which in turn makes the last prediction decision. We conduct experiments comparing our framework with one-VS-one and Directed Acyclic Graph (DAG) using Support Vector Machines. Additionally, we compare our model with well-known ensemble models, namely, AdaBoost and Bagging, Our results indicate that prediction accuracy is comparable to other methodologies with the advantage of consuming less prediction time.
KW - 2-tier framework
KW - Ensemble
KW - Multiclass
KW - Support Vector Machines
UR - http://www.scopus.com/inward/record.url?scp=84945927688&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84945927688&partnerID=8YFLogxK
U2 - 10.1109/ICCSA.2015.16
DO - 10.1109/ICCSA.2015.16
M3 - Conference contribution
AN - SCOPUS:84945927688
T3 - Proceedings - 15th International Conference on Computational Science and Its Applications, ICCSA 2015
SP - 77
EP - 81
BT - Proceedings - 15th International Conference on Computational Science and Its Applications, ICCSA 2015
A2 - Misra, Sanjay
A2 - Apduhan, Bernady O.
A2 - Gavrilova, Marina L.
A2 - Taniar, D.
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Computational Science and Its Applications, ICCSA 2015
Y2 - 22 June 2015 through 25 June 2015
ER -