期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：718-722
出版社：Newswood and International Association of Engineers
摘要：Incremental Attribute Learning (IAL) has been
treated as an applicable approach for solving high-dimensional
classification problems, and it has been successfully applied in
many other predictive algorithms, like Neural Networks (NN),
Genetic Algorithms (GA), and Particle Swarm Optimization
(PSO). So far, it is not employed for K Nearest Neighbor (KNN),
another very popular algorithm in pattern classification.
Therefore, in this paper IAL is attempted to be used with KNN.
Experiments based on some benchmarks showed that such an
approach can works very fast and the results are also
acceptable.
