TY - GEN
T1 - Ranking-based fuzzy min-max classification neural network
AU - Xue, Lingli
AU - Huang, Wei
AU - Wang, Jinsong
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - The performance of fuzzy min-max classification neural network (FMM) is affected by the input sequence of training set patterns. This paper proposes a ranking-based fuzzy min-max Classification Neural Network (RFMM) to overcome this shortcoming. RFMM improves FMM through the following three aspects. First, RFMM ranks the input order of the training set patterns according to their membership degree to the center point of same class, so that the finally constructed network is fixed and does not depend on the input order of the training set. Second, a new membership function based on Manhattan distance is constructed, which overcomes the problem that the membership degree obtained by the membership function in the FMM will not decrease steadily with the increase of the distance between the input pattern and the hyperbox. At last, RFMM uses the method based on individual contour coefficient to classify the patterns in overlapping regions, which overcomes the problem that when the FMM eliminates the overlapping region by shrinking hyperboxes, the membership degree of the patterns in the contracted region to the class they belong is changed. Experimental results show that RFMM has better learning ability, and compared with other FMM methods, RFMM shows higher classification accuracy and lower network complexity.
AB - The performance of fuzzy min-max classification neural network (FMM) is affected by the input sequence of training set patterns. This paper proposes a ranking-based fuzzy min-max Classification Neural Network (RFMM) to overcome this shortcoming. RFMM improves FMM through the following three aspects. First, RFMM ranks the input order of the training set patterns according to their membership degree to the center point of same class, so that the finally constructed network is fixed and does not depend on the input order of the training set. Second, a new membership function based on Manhattan distance is constructed, which overcomes the problem that the membership degree obtained by the membership function in the FMM will not decrease steadily with the increase of the distance between the input pattern and the hyperbox. At last, RFMM uses the method based on individual contour coefficient to classify the patterns in overlapping regions, which overcomes the problem that when the FMM eliminates the overlapping region by shrinking hyperboxes, the membership degree of the patterns in the contracted region to the class they belong is changed. Experimental results show that RFMM has better learning ability, and compared with other FMM methods, RFMM shows higher classification accuracy and lower network complexity.
KW - Fuzzy Min-Max neural network
KW - Hyperbox fuzzy set
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85092113004&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60029-7_33
DO - 10.1007/978-3-030-60029-7_33
M3 - Conference contribution
AN - SCOPUS:85092113004
SN - 9783030600280
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 352
EP - 364
BT - Web Information Systems and Applications - 17th International Conference, WISA 2020, Proceedings
A2 - Wang, Guojun
A2 - Lin, Xuemin
A2 - Hendler, James
A2 - Song, Wei
A2 - Xu, Zhuoming
A2 - Liu, Genggeng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Web Information Systems and Applications, WISA 2020
Y2 - 23 September 2020 through 25 September 2020
ER -