Data Classification with Radial Basis Function Networks Based on a Novel Kernel Density Estimation Algorithm
Date Issued
2002
Date
2002
Author(s)
DOI
2006092712290078966
Abstract
This paper presents a novel learning algorithm for efficient construction of the radial basis function
(RBF) networks that can deliver the same level of accuracy as the support vector machines (SVM)
in data classification applications. The proposed learning algorithm works by constructing one
RBF sub-network to approximate the probability density function of each class of objects in the
training data set. With respect to algorithm design, the main distinction of the proposed learning
algorithm is the novel kernel density estimation algorithm that features an average time complexity
of O(nlogn), where n is the number of samples in the training data set. One important advantage
of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning
algorithm generally takes far less time to construct a data classifier with an optimized parameter
setting. This feature is of significance for many contemporary applications, in particular, for
those applications in which new objects are continuously added into an already large database.
Another desirable feature of the proposed learning algorithm is that the RBF network constructed
is capable of carrying out data classification with more than two classes of objects in one single
run. In other words, unlike SVM, it does not need to invoke mechanisms such as one-against-one
or one-against-all for handling datasets with more than two classes of objects. The comparison
with SVM is of particular interest, because it has been shown in a number of recent studies that
SVM generally are able to deliver higher level of accuracy than the other existing data
classification algorithms. As the proposed learning algorithm is instance-based, the data
reduction issue is also addressed in this paper. One interesting observation in this regard is that,
for all three data sets used in data reduction experiments, the number of training samples remaining
after a na?ve data reduction mechanism is applied is quite close to the number of support vectors
identified by the SVM software. This paper also compares the performance of the RBF networks
constructed with the proposed learning algorithm and those constructed with a conventional
cluster-based learning algorithm. The most interesting observation learned is that, with respect to
data classification, the distributions of training samples near the boundaries between different
classes of objects carry more crucial information than the distributions of samples in the inner parts
of the clusters.
Subjects
radial basis function (RBF) network
kernel density estimation
data classification
machine learning
neural network
Publisher
臺北市:國立臺灣大學資訊工程學系
Type
other
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