期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2019
卷号：11
页码：161-169
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：Selection of appropriate nearest neighbors greatly
affects predictive accuracy of nearest neighbor classifier.
Feature similarity is often used to decide the set of k nearest
neighbors. Predictive accuracy of multi-label kNN could further
be enhanced if in addition to the feature similarity, difference in
feature values and dissimilarity of the instance labels are also
taken into account to decide the set of k nearest neighbors. This
paper deals with an algorithm called “ML-FLD” that not only
takes into account features similarity of the instances, but also
considers feature difference and label dissimilarity in order to
decide the k nearest neighbors of a given unseen instance for
the prediction of its labels. The algorithm when tested using
well-known datasets and checked with the existing well known
algorithms, provides better performance in terms of examplebased
metrics such as hamming loss, ranking loss, one error,
coverage, average precision, accuracy, F measure as well as
label-based metrics like macro-averaged and micro-averaged F
measure.
