Assassin: an Automatic classification system based on algorithm selection

Summary: Assassin automates algorithm selection for classification tasks via meta-learning and reinforced policy, training a meta-classifier to recommend algorithms from past tasks. Genetic search then tunes hyperparameters for the chosen model, demonstrated on OpenML with a user-friendly interface for parameter customization. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12463
Venue: VLDB
Year: 2021
Pagerank: 4.5309467e-05
Overall Rank: 8,382 | 41.69%
DOI: 10.14778/3476311.3476336

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,494	SubStrat: A Subset-Based Optimization Strategy for Faster AutoML	2023	VLDB	4.7180617e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,384	Oracle AutoML: A Fast and Predictive AutoML Pipeline	2020	VLDB	8.925354e-05

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