@inproceedings{fc654defdecc45a8976898b40082891f,
title = "Indecisive trees for classification and prediction of knee osteoarthritis",
abstract = "Random forests are widely used for classification and regression tasks in medical image analysis. Each tree in the forest contains binary decision nodes that choose whether a sample should be passed to one of two child nodes. We demonstrate that replacing this with something less decisive, where some samples may go to both child nodes, can improve performance for both individual trees and whole forests. Introducing a soft decision at each node means that a sample may propagate to multiple leaves. The tree output should thus be a weighted sum of the individual leaf values. We show how the leaves can be optimised to improve performance and how backpropagation can be used to optimise the parameters of the decision functions at each node. Finally, we show that the new method outperforms an equivalent random forest on a disease classification and prediction task.",
keywords = "Decision trees, Optimisation, Random forests",
author = "Luca Minciullo and Bromiley, {Paul A.} and Felson, {David T.} and Cootes, {Timothy F.}",
year = "2017",
doi = "10.1007/978-3-319-67389-9_33",
language = "English",
isbn = "9783319673882",
volume = "10541 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "283--290",
booktitle = "Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings",
address = "United States",
note = "8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 ; Conference date: 10-09-2017 Through 10-09-2017",
}