Book list for ensemble learning and stacking

I have been studying blending, stacking machine learning models for a while, as written in previous post: [note] Ensembling multiple machine learning models. I have applied blending - extended from general version to my recent Kaggle competitions, however, it didn’t perform as good as I had expected.

To strengthen both the theory and practice about ensembling, stacking, blending, I conducted some survey online and added these two books to my to-read list:

Ensemble Methods: Foundations and Algorithms
Author: Zhi-Hua Zhou, 2012

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition
Authors: Ian H. Witten, Eibe Frank, Mark A. Hall, 2011

People’s recommendations

“The authors provide enough theory to enable practical application, and it is this practical focus that separates this book from most, if not all, other books on this subject.” -Dorian Pyle, Director of Modeling at Numetrics

张垚 from ZHIHU: 周志华老师的Ensemble Methods: Foundations and Algorith ,我正在读这本书,难度略高, 不过读起来很过瘾。

wepon from ZHIHU: Data Mining:Practical Machine Learning Tools and Techniques - 第八章认真读一读,特别是Further Reading中提到的那些作者,论文。然后比赛实战,看榜首大牛们的solution,自己照着做一两次。

Reference / Additional Information

WEKA
ZHIHU - 请问学习 ensemble learning 要从哪里开始呢?
Kaggle Forum - Blended Ensemble - Why it never works for me ?