TY - GEN
T1 - An Improved Approach to Ordinal Classification
AU - WANG, Donghui
AU - ZHAI, Junhai
AU - ZHU, Hong
AU - WANG, Xizhao
N1 - This research is supported by the national natural science foundation of China (61170040, 71371063), by the key scientific research foundation of education department of Hebei Province (ZD20131028), by the scientific research foundation of education department of Hebei Province (Z2012101), and by the natural science foundation of Hebei Province (F2013201110, F2013201220).
PY - 2014
Y1 - 2014
N2 - A simple ordinal classification approach (SOCA) has been proposed by Frank and Hall. SOCA is a general method, any classification algorithm such as C4.5, k nearest neighbors (KNN) algorithm and extreme learning machine (ELM) etc. can be applied to this approach. We find that in SOCA only ordering information of decision attribute is used to classify objects but the ordering information of conditional attributes is not considered. Furthermore we experimentally find that ordering information of conditional attributes can also improve the generalization ability of the classification method. In this paper, we propose an improved ordinal classification methodology by employing the ordering information of both condition and decision attributes. In addition, we analyze the sensitivity of the SOCA on performance to the underlying classification algorithms, for instance, C4.5, KNN and ELM. A number of experiments are conducted and the experimental results show that the proposed method is feasible and effective.
AB - A simple ordinal classification approach (SOCA) has been proposed by Frank and Hall. SOCA is a general method, any classification algorithm such as C4.5, k nearest neighbors (KNN) algorithm and extreme learning machine (ELM) etc. can be applied to this approach. We find that in SOCA only ordering information of decision attribute is used to classify objects but the ordering information of conditional attributes is not considered. Furthermore we experimentally find that ordering information of conditional attributes can also improve the generalization ability of the classification method. In this paper, we propose an improved ordinal classification methodology by employing the ordering information of both condition and decision attributes. In addition, we analyze the sensitivity of the SOCA on performance to the underlying classification algorithms, for instance, C4.5, KNN and ELM. A number of experiments are conducted and the experimental results show that the proposed method is feasible and effective.
KW - Decision tree
KW - Monotonic classification
KW - Ordinal classification
KW - Rank mutual information
UR - http://www.scopus.com/inward/record.url?scp=84917735140&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-45652-1_4
DO - 10.1007/978-3-662-45652-1_4
M3 - Conference paper (refereed)
AN - SCOPUS:84917735140
SN - 9783662456514
T3 - Communications in Computer and Information Science
SP - 33
EP - 42
BT - Machine Learning and Cybernetics : 13th International Conference, Proceedings
A2 - WANG, Xizhao
A2 - HE, Qiang
A2 - CHAN, Patrick P.K.
A2 - PEDRYCZ, Witold
PB - Springer Berlin
T2 - 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014
Y2 - 13 July 2014 through 16 July 2014
ER -