@inproceedings{493011f7439b4d72b70635a7fead8175,
title = "Top-k parametrized boost",
abstract = "Ensemble methods such as AdaBoost are popular machine learning methods that create highly accurate classifier by combining the predictions from several classifiers. We present a parametrized method of AdaBoost that we call Top-k Parametrized Boost. We evaluate our and other popular ensemble methods from a classification perspective on several real datasets. Our empirical study shows that our method gives the minimum average error with statistical significance on the datasets.",
keywords = "AdaBoost, Ensemble methods, statistical significance",
author = "Turki Turki and Muhammad Ihsan and Nouf Turki and Jie Zhang and Usman Roshan and Zhi Wei",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014 ; Conference date: 10-12-2014 Through 12-12-2014",
year = "2014",
doi = "10.1007/978-3-319-13817-6_10",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "91--98",
editor = "Rajendra Prasath and Philip O{\textquoteright}Reilly and Thangairulappan Kathirvalavakumar",
booktitle = "Mining Intelligence and Knowledge Exploration - 2nd International Conference, MIKE 2014, Proceedings",
address = "Germany",
}