Interpretability and Effectiveness of Machine Learning Methods for Sequence Mining in Various Domains
March 2021
Pages 1113 - 1114
Abstract
There is a diverse variety of demographic data that can be analyzed with modern methods of data mining to achieve better results. On the one hand, the main chosen task is to compare different methods for the next event prediction and gender prediction, on the other hand, we pay special attention to interpretable patterns describing demographic behavior in the studied problems. There were considered interpretable methods as decision trees and their ensembles and semi- or non-interpretable methods, such as the SVM method with different customized kernels tailored for demographers' needs and neural networks, respectively. The best accuracy results were obtained with two-channel Convolutional Neural Networks.
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Index Terms
- Interpretability and Effectiveness of Machine Learning Methods for Sequence Mining in Various Domains
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Published In
March 2021
1192 pages
ISBN:9781450382977
DOI:10.1145/3437963
- General Chairs:
- Liane Lewin-Eytan,
- David Carmel,
- Elad Yom-Tov,
- Program Chairs:
- Eugene Agichtein,
- Evgeniy Gabrilovich
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Published: 08 March 2021
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- Basic Research Program at the National Research University Higher School of Economics and funded by the Russian Academic Excelle
- Faculty of Social Sciences National Research University Higher School of Economics
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WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining
March 8 - 12, 2021
Virtual Event, Israel
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