Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Machine Learning
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Nearest neighbors in high-dimensional data: the emergence and influence of hubs
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
PLANET: massively parallel learning of tree ensembles with MapReduce
Proceedings of the VLDB Endowment
MIForests: multiple-instance learning with randomized trees
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Data mining for credit card fraud: A comparative study
Decision Support Systems
On-line multi-view forests for tracking
Proceedings of the 32nd DAGM conference on Pattern recognition
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
The Journal of Machine Learning Research
Prediction of DNA-binding propensity of proteins by the ball-histogram method
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Foundations and Trends® in Computer Graphics and Vision
Friends don't lie: inferring personality traits from social network structure
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Joint classification-regression forests for spatially structured multi-object segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
An expert system for automatically pruning vines
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Randomness and sparsity induced codebook learning with application to cancer image classification
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Using behavioral data to identify interviewer fabrication in surveys
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Apparel classification with style
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
SoCo: a social network aided context-aware recommender system
Proceedings of the 22nd international conference on World Wide Web
On learning-based methods for design-space exploration with high-level synthesis
Proceedings of the 50th Annual Design Automation Conference
ViziCal: accurate energy expenditure prediction for playing exergames
Proceedings of the 26th annual ACM symposium on User interface software and technology
Improving the performance of neural networks with random forest in detecting network intrusions
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Accurate probability calibration for multiple classifiers
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Computational Statistics & Data Analysis
The impact of multinationality on firm value: A comparative analysis of machine learning techniques
Decision Support Systems
Predicting execution time of machine learning tasks for scheduling
International Journal of Hybrid Intelligent Systems
Hybrid random subsample classifier ensemble for high dimensional data sets
International Journal of Hybrid Intelligent Systems
Computation of models for prediction of blood brain barrier permeability using molecular descriptors
Journal of Computational Methods in Sciences and Engineering
Hi-index | 0.00 |
In this paper we perform an empirical evaluation of supervised learning on high-dimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and study the effect of increasing dimensionality on the performance of the learning algorithms. Our findings are consistent with previous studies for problems of relatively low dimension, but suggest that as dimensionality increases the relative performance of the learning algorithms changes. To our surprise, the method that performs consistently well across all dimensions is random forests, followed by neural nets, boosted trees, and SVMs.