The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
An empirical evaluation of supervised learning in high dimensions
Abstract
In this paper we perform an empirical evaluation of supervised learning on high-dimensional data. We evaluate perfor-mance on three metrics: accuracy, AUC, and squared loss and study the eect of increas-ing dimensionality on the performance of the learning algorithms. Our ndings are con-sistent with previous studies for problems of relatively low dimension, but suggest that as dimensionality increases the relative perfor-mance of the learning algorithms changes. To our surprise, the method that performs consistently well across all dimensions is ran-dom forests, followed by neural nets, boosted trees, and SVMs. 1.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280
Cited by
Data mining for credit card fraud: a comparative study
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.008 · Cited by 1,108
Criminisi, A., Shotton, J., & Konukoglu, E. (2012) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000035 · Cited by 677
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617
A Bag-of-Features Framework to Classify Time Series
Baydogan, M. G., Runger, G., & Tuv, E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.72 · Cited by 361
Pardakhti, M., Moharreri, E., Wanik, D., Suib, S. L., & Srivastava, R. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.7b00056 · Cited by 254
Sewa db: A rich database for audio-visual emotion and sentiment research in the wild
Kossaifi, J., Walecki, R., Panagakis, Y., Shen, J., Schmitt, M., Ringeval, F., Han, J., Pandit, V., Toisoul, A., Schuller, B., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2944808 · Cited by 243
AFEW-VA database for valence and arousal estimation in-the-wild
Kossaifi, J., Tzimiropoulos, G., Todorovic, S., & Pantic, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.02.001 · Cited by 236
Suryanarayana, G., Lago, J., Geysen, D., Aleksiejuk, P., & Johansson, C. (2018) · Energy · doi:10.1016/j.energy.2018.05.111 · Cited by 155
Airline itinerary choice modeling using machine learning
Lhéritier, A., Bocamazo, M., Delahaye, T., & Acuna-Agost, R. (2019) · Journal of Choice Modelling · doi:10.1016/j.jocm.2018.02.002 · Cited by 114
Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy
Askland, K. D., Garnaat, S., Sibrava, N. J., Boisseau, C. L., Strong, D., Mancebo, M., Greenberg, B., Rasmussen, S., & Eisen, J. (2015) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1463 · Cited by 75
Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest
Shadman Roodposhti, M., Aryal, J., Lucieer, A., & Bryan, B. A. (2019) · Entropy · doi:10.3390/e21010078 · Cited by 65
Enhancing intrusion detection with feature selection and neural network
Wu, C., & Li, W. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22397 · Cited by 53
Metadata
- Authors
- Caruana, R., Karampatziakis, N., & Yessenalina, A.
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 96-103
- OpenAlex
- W2128302979
- Cited by
- 524
- References
- 2
- FWCI
- 9.68
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
