Details
| Originalsprache | Deutsch |
|---|---|
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - Juli 2018 |
| Extern publiziert | Ja |
| Veranstaltung | International Workshop on Automatic Machine Learning 2018 - Stockholm, Schweden Dauer: 14 Juli 2018 → 14 Juli 2018 |
Workshop
| Workshop | International Workshop on Automatic Machine Learning 2018 |
|---|---|
| Kurztitel | ICML 2018 AutoML Workshop |
| Land/Gebiet | Schweden |
| Ort | Stockholm |
| Zeitraum | 14 Juli 2018 → 14 Juli 2018 |
Abstract
from performance issues when dealing with larger datasets. In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length. We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.
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2018. Beitrag in International Workshop on Automatic Machine Learning 2018, Stockholm, Schweden.
Publikation: Konferenzbeitrag › Paper › Forschung › Peer-Review
}
TY - CONF
T1 - ML-Plan for Unlimited-Length Machine Learning Pipelines
AU - Wever, Marcel
AU - Mohr, Felix
AU - Hüllermeier, Eyke
PY - 2018/7
Y1 - 2018/7
N2 - In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated. Various AutoML tools have already been introduced to provide out-of-the-box machine learning functionality. More specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand. Except for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline. However, as TPOT follows an evolutionary approach, it suffersfrom performance issues when dealing with larger datasets. In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length. We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.
AB - In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated. Various AutoML tools have already been introduced to provide out-of-the-box machine learning functionality. More specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand. Except for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline. However, as TPOT follows an evolutionary approach, it suffersfrom performance issues when dealing with larger datasets. In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length. We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.
M3 - Paper
T2 - International Workshop on Automatic Machine Learning 2018
Y2 - 14 July 2018 through 14 July 2018
ER -