Multi-class and Multi-label classication of Darkweb Data
abstract: In this research, I try to solve multi-class multi-label classication problem, where the goal is to automatically assign one or more labels(tags) to discussion topics seen in deepweb. I observed natural hierarchy in our dataset, and I used dierent techniques to ensure hierarchical integ...
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| Format: | Dissertation |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/2286/R.I.48469 |
| Summary: | abstract: In this research, I try to solve multi-class multi-label classication problem, where
the goal is to automatically assign one or more labels(tags) to discussion topics seen
in deepweb. I observed natural hierarchy in our dataset, and I used dierent
techniques to ensure hierarchical integrity constraint on the predicted tag list. To
solve `class imbalance' and `scarcity of labeled data' problems, I developed semisupervised
model based on elastic search(ES) document relevance score. I evaluate
our models using standard K-fold cross-validation method. Ensuring hierarchical
integrity constraints improved F1 score by 11.9% over standard supervised learning,
while our ES based semi-supervised learning model out-performed other models in
terms of precision(78.4%) score while maintaining comparable recall(21%) score. === Dissertation/Thesis === Masters Thesis Computer Science 2018 |
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