@inproceedings{181b3ceeb75c490f9a84e97915c38461,
title = "Extreme learning machine for interval-valued data",
abstract = "Extreme learning machine (ELM) is a fast learning algorithm for single hidden layer feed-forward neural networks, but it only can deal with the data sets with numerical attributes. Interval-valued data is considered as a direct attempt to extend precise real-valued data to imprecise scenarios. To deal with imprecise data, this paper proposes three extreme learning machine (ELM) models for interval-valued data. Mid-point and range of the interval are selected as the variables in the first model as in previous works. The second model selects endpoints as variables and produces better performance than model 1. The third model, a constrained ELM for interval-valued data, is built to guarantee the left bound is always smaller than its right bound. Three different standards are used to test the effectiveness of the three models, and experimental results show that the latter two models offer better performances than the former one.",
keywords = "Endpoint of interval, Extreme learning machine, Interval-valued, Mid-point of interval, Range of interval",
author = "Shixin ZHAO and Xizhao WANG",
note = "This work is supported in part by Natural Nature Science Foundation of China (No. 61170040, 71371063, 71201111).; 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 ; Conference date: 13-07-2014 Through 16-07-2014",
year = "2014",
doi = "10.1007/978-3-662-45652-1_39",
language = "English",
isbn = "9783662456514",
series = "Communications in Computer and Information Science",
publisher = "Springer Berlin",
pages = "388--399",
editor = "Xizhao WANG and Qiang HE and CHAN, {Patrick P.K.} and Witold PEDRYCZ",
booktitle = "Machine Learning and Cybernetics : 13th International Conference, Proceedings",
address = "Germany",
}