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2020 – today
- 2021
[j1]Inna Kolyshkina, Simeon Simoff:
Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach. Frontiers Big Data 4: 660206 (2021)- [c6]Inna Kolyshkina, Simeon Simoff:
The CRISP-ML Approach to Handling Causality and Interpretability Issues in Machine Learning. IEEE BigData 2021: 2306-2312
2010 – 2019
- 2019
- [c5]Inna Kolyshkina
, Simeon Simoff:
Interpretability of Machine Learning Solutions in Industrial Decision Engineering. AusDM 2019: 156-170 - [c4]Inna Kolyshkina, Simeon Simoff:
Comprehensibility for Decision Intelligence Engineering. ICDM (Posters) 2019: 80-94 - 2013
- [c3]Inna Kolyshkina, Marcus Brownlow, Jarrad Taylor:
Improving Every Child's Chance in Life. ICDM Workshops 2013: 180-184
2000 – 2009
- 2007
- [c2]Inna Kolyshkina, Simeon J. Simoff:
Customer Analytics Projects: Addressing Existing Problems with a Process that Leads to Success. AusDM 2007: 13-19 - [e1]Peter Christen, Paul J. Kennedy, Jiuyong Li, Inna Kolyshkina, Graham J. Williams:
Data Mining and Analytics 2007, Proceedings of the Sixth Australasian Data Mining Conference (AusDM 2007), Gold Coast, Queensland, Australia, December 3-4, 2007, Proceedings. CRPIT 70, Australian Computer Society 2007, ISBN 978-1-920682-51-4 [contents] - 2006
- [p1]Inna Kolyshkina, Marcel van Rooyen:
Text Mining for Insurance Claim Cost Prediction. Selected Papers from AusDM 2006: 192-202 - 2002
- [c1]Inna Kolyshkina, Richard Brookes:
Case Study: Modeling Risk in Health Insurance - A Data Mining Approach. AusDM 2002: 13-16
Coauthor Index
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