TY - GEN
T1 - Naive Bayesian Classifier Based on Neighborhood Probability
AU - LIU, Jame N.K.
AU - HE, Yulin
AU - WANG, Xizhao
AU - HU, Yanxing
N1 - This work is in part supported by GRF grant 5237/08E, CRG grant G-U756 of The Hong Kong Polytechnic University, The National Natural Science Foundation of China 61170040.
PY - 2012
Y1 - 2012
N2 - When calculating the class-conditional probability of continuous attributes with naive Bayesian classifier (NBC) algorithm, the existing methods usually make use of the superposition of many normal distribution probability density functions to fit the true probability density function. Accordingly, the value of the class-conditional probability is equal to the sum of values of normal distribution probability density functions. In this paper, we propose a NPNBC model, i.e. the naive Bayesian classifier based on the neighborhood probability. In NPNBC, when calculating the class-conditional probability for a continuous attribute value in the given unknown example, a small neighborhood is created for the continuous attribute value in every normal distribution probability density function. So, the neighborhood probabilities for each normal distribution probability density function can be obtained. The sum of these neighborhood probabilities is the class-conditional probability for the continuous attribute value in NPNBC. Our experimental results demonstrate that NPNBC can obtain the remarkable performance in classification accuracy when compared with the normal method and the kernel method. In addition, we also investigate the relationship between the classification accuracy of NPNBC and the value of neighborhood.
AB - When calculating the class-conditional probability of continuous attributes with naive Bayesian classifier (NBC) algorithm, the existing methods usually make use of the superposition of many normal distribution probability density functions to fit the true probability density function. Accordingly, the value of the class-conditional probability is equal to the sum of values of normal distribution probability density functions. In this paper, we propose a NPNBC model, i.e. the naive Bayesian classifier based on the neighborhood probability. In NPNBC, when calculating the class-conditional probability for a continuous attribute value in the given unknown example, a small neighborhood is created for the continuous attribute value in every normal distribution probability density function. So, the neighborhood probabilities for each normal distribution probability density function can be obtained. The sum of these neighborhood probabilities is the class-conditional probability for the continuous attribute value in NPNBC. Our experimental results demonstrate that NPNBC can obtain the remarkable performance in classification accuracy when compared with the normal method and the kernel method. In addition, we also investigate the relationship between the classification accuracy of NPNBC and the value of neighborhood.
KW - kernel method
KW - naive Bayesian classifier
KW - neighborhood probability
KW - normal method
KW - NPNBC
UR - http://www.scopus.com/inward/record.url?scp=84868094567&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31718-7_12
DO - 10.1007/978-3-642-31718-7_12
M3 - Conference paper (refereed)
AN - SCOPUS:84868094567
SN - 9783642317170
T3 - Communications in Computer and Information Science
SP - 112
EP - 121
BT - Advances in Computational Intelligence : 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Proceedings
A2 - GRECO, Salvatore
A2 - BOUCHON-MEUNIER, Bernadette
A2 - COLETTI, Giulianella
A2 - FEDRIZZI, Mario
A2 - MATARAZZO, Benedetto
A2 - YAGER, Ronald R.
PB - Springer Berlin
T2 - 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012
Y2 - 9 July 2012 through 13 July 2012
ER -