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- research-articleOctober 2023
Avoiding the impact of “Filter Bubbles” – Take “Internet Doctor” as example
ICMHI '23: Proceedings of the 2023 7th International Conference on Medical and Health InformaticsPages 150–154https://doi.org/10.1145/3608298.3608326Over-personalized recommendation algorithms have led to various invisible filtering bubbles in the internet society, which constantly affect the internet environment and social ecology, causing some social impacts worth attention and some social ...
- research-articleApril 2021
Feature Construction for Meta-heuristic Algorithm Recommendation of Capacitated Vehicle Routing Problems
ACM Transactions on Evolutionary Learning and Optimization (TELO), Volume 1, Issue 1Article No.: 3, Pages 1–28https://doi.org/10.1145/3447540The algorithm recommendation is attracting increasing attention in solving real-world capacitated vehicle routing problems (CVRPs), due to the fact that existing meta-heuristic algorithms often show different performances on different CVRPs. To ...
- short-paperOctober 2018
A Hybrid Approach for Automatic Model Recommendation
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge ManagementPages 1623–1626https://doi.org/10.1145/3269206.3269299One of the challenges of automating machine learning applications is the automatic selection of an algorithmic model for a given problem. We present AutoDi, a novel and resource-efficient approach for model selection. Our approach combines two sources ...
- research-articleJune 2016
CoDAR: Revealing the Generalized Procedure & Recommending Algorithms of Community Detection
SIGMOD '16: Proceedings of the 2016 International Conference on Management of DataPages 2181–2184https://doi.org/10.1145/2882903.2899386Community detection has attracted great interest in graph analysis and mining during the past decade, and a great number of approaches have been developed to address this problem. However, the lack of a uniform framework and a reasonable evaluation ...
- research-articleApril 2016
A meta-learning framework for algorithm recommendation in software fault prediction
SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied ComputingPages 1486–1491https://doi.org/10.1145/2851613.2851788Software fault prediction is a significant part of software quality assurance and it is commonly used to detect faulty software modules based on software measurement data. Several machine learning based approaches have been proposed for generating ...
- ArticleDecember 2012
Clustering algorithm recommendation: a meta-learning approach
SEMCCO'12: Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic ComputingPages 143–150https://doi.org/10.1007/978-3-642-35380-2_18Meta-learning is a technique that aims at understanding what types of algorithms solve what kinds of problems. Clustering, by contrast, divides a dataset into groups based on the objects' similarities without the need of previous knowledge about the ...
- articleMarch 2003
Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results
We present a meta-learning method to support selection of candidate learning algorithms. It uses a k-Nearest Neighbor algorithm to identify the datasets that are most similar to the one at hand. The distance between datasets is assessed using a ...