@inproceedings{d861541ffe824e1281a308db8a7d377d,
title = "Incremental attribute learning based on KNN",
abstract = "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.",
author = "Ting Wang and Guan, {Sheng Uei} and Zhihong Wang",
note = "Publisher Copyright: {\textcopyright} 2018 Newswood Limited. All rights reserved.; 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 ; Conference date: 14-03-2018 Through 16-03-2018",
year = "2018",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
editor = "Oscar Castillo and Feng, {David Dagan} and A.M. Korsunsky and Craig Douglas and Ao, {S. I.}",
booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018",
}