Proceedings ArticleDOI
Framework for integration of domain knowledge into logistic regression
TLDR
This work proposes a framework that integrates domain knowledge in form of hierarchies into machine learning models, namely logistic regression, and shows that the proposed framework yields better results compared to standardLogistic regression model.Abstract:
Traditionally, machine learning extracts knowledge solely based on data. However, huge volume of knowledge is available in other sources which can be included into machine learning models. Still, domain knowledge is rarely used in machine learning. We propose a framework that integrates domain knowledge in form of hierarchies into machine learning models, namely logistic regression. Integration of the hierarchies is done by using stacking (stacked generalization). We show that the proposed framework yields better results compared to standard logistic regression model. The framework is tested on the binary classification problem for predicting 30-days hospital readmission. Results suggest that the proposed framework improves AUC (area under the curve) compared to logistic regression models unaware of domain knowledge by 9% on average.read more
Citations
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Proceedings ArticleDOI
Incorporating Prior Domain Knowledge into Deep Neural Networks
TL;DR: This paper proposes domain adapted neural networks (DANN) to explore how domain knowledge can be integrated into model training for deep networks, and incorporates loss terms for knowledge available as monotonicity constraints and approximation constraints.
Proceedings ArticleDOI
Mt-Gcn For Multi-Label Audio Tagging With Noisy Labels
TL;DR: MT-GCN is presented, a Multi-task Learning based Graph Convolutional Network that learns domain knowledge from ontology that outperforms the baseline methods by a significant margin.
Journal ArticleDOI
A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction
TL;DR: It is evident that the proposed stacking-based model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.
Book ChapterDOI
Profiling Environmental Conditions from DNA.
TL;DR: It is demonstrated that genomic DNA may also encode sufficient information about some environmental features of an organism’s habitat for a machine learning model to reveal them, although there seem to be exceptions, which lead directly to the question of whether over evolutionary history, DNA itself is actually also a repository of information related to the environment where the lineage has developed.
Proceedings ArticleDOI
A Roadmap to Domain Knowledge Integration in Machine Learning
Himel Das Gupta,Victor S. Sheng +1 more
TL;DR: This paper gives a brief overview of different forms of knowledge integration and their performance in certain machine learning tasks.
References
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Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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An introduction to statistical learning
TL;DR: An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years.
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The Description Logic Handbook: Theory, Implementation and Applications
TL;DR: The Description Logic Handbook as mentioned in this paper provides a thorough account of the subject, covering all aspects of research in this field, namely: theory, implementation, and applications, and can also be used for self-study or as a reference for knowledge representation and artificial intelligence courses.
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Learning Logical Definitions from Relations
TL;DR: foil is a system that learns Horn clauses from data expressed as relations, based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism.
Proceedings ArticleDOI
MetaCost: a general method for making classifiers cost-sensitive
TL;DR: A principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it is proposed, called MetaCost, which treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it.