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- W2897897876 abstract "One 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 of information: metafeatures extracted from the data itself and word-embedding features extracted from a large corpus of academic publications. This hybrid approach enables AutoDi to select top-performing algorithms both for widely and rarely used datasets by utilizing its two types of feature sets. We demonstrate the effectiveness of our proposed approach on a large dataset of 119 datasets and 179 classification algorithms grouped into 17 families. We show that AutoDi can reach an average of 98.8% of optimal accuracy and select the optimal classification algorithm in 49.5% of all cases." @default.
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- W2897897876 date "2018-10-17" @default.
- W2897897876 modified "2023-10-13" @default.
- W2897897876 title "A Hybrid Approach for Automatic Model Recommendation" @default.
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- W2897897876 doi "https://doi.org/10.1145/3269206.3269299" @default.
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