@inproceedings{40abaa3484d84dca8e0c1c6f7f6ef1a0,
title = "Differentiation and integration of machine learning feature vectors",
abstract = "This paper presents a new approach to the production of feature maps for the improvement of classification in machine learning. The idea is based on a calculus of differentiation and integration of feature vectors, which can be viewed as functions on a metric space or network. Based on this we propose a novel network-based binary machine learning classifier. We illustrate our method using molecular networks alone to distinguish phenotypes, including cancer types and subtypes. We include feature sets derived from diseasespecific gene co-expression networks in different cancer data sets using The Cancer Genome Atlas (TCGA) along with other previously published studies. We also illustrate our network-based predictor on another data type, based on infrared spectroscopy of lung cancer tissue.",
keywords = "Cancer, Classification, Gene coexpression networks, Kernel method",
author = "Xinying Mu and Pavel, \{Ana B.\} and Mark Kon",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 ; Conference date: 18-12-2016 Through 20-12-2016",
year = "2017",
month = jan,
day = "31",
doi = "10.1109/ICMLA.2016.79",
language = "English",
series = "Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "611--616",
booktitle = "Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016",
address = "United States",
}