3,589 research outputs found
Improved Weighted Random Forest for Classification Problems
Several studies have shown that combining machine learning models in an
appropriate way will introduce improvements in the individual predictions made
by the base models. The key to make well-performing ensemble model is in the
diversity of the base models. Of the most common solutions for introducing
diversity into the decision trees are bagging and random forest. Bagging
enhances the diversity by sampling with replacement and generating many
training data sets, while random forest adds selecting a random number of
features as well. This has made the random forest a winning candidate for many
machine learning applications. However, assuming equal weights for all base
decision trees does not seem reasonable as the randomization of sampling and
input feature selection may lead to different levels of decision-making
abilities across base decision trees. Therefore, we propose several algorithms
that intend to modify the weighting strategy of regular random forest and
consequently make better predictions. The designed weighting frameworks include
optimal weighted random forest based on ac-curacy, optimal weighted random
forest based on the area under the curve (AUC), performance-based weighted
random forest, and several stacking-based weighted random forest models. The
numerical results show that the proposed models are able to introduce
significant improvements compared to regular random forest
On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
With the coming data deluge from synoptic surveys, there is a growing need
for frameworks that can quickly and automatically produce calibrated
classification probabilities for newly-observed variables based on a small
number of time-series measurements. In this paper, we introduce a methodology
for variable-star classification, drawing from modern machine-learning
techniques. We describe how to homogenize the information gleaned from light
curves by selection and computation of real-numbered metrics ("feature"),
detail methods to robustly estimate periodic light-curve features, introduce
tree-ensemble methods for accurate variable star classification, and show how
to rigorously evaluate the classification results using cross validation. On a
25-class data set of 1542 well-studied variable stars, we achieve a 22.8%
overall classification error using the random forest classifier; this
represents a 24% improvement over the best previous classifier on these data.
This methodology is effective for identifying samples of specific science
classes: for pulsational variables used in Milky Way tomography we obtain a
discovery efficiency of 98.2% and for eclipsing systems we find an efficiency
of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is
superior to other machine-learned methods in terms of accuracy, speed, and
relative immunity to features with no useful class information; the RF
classifier can also be used to estimate the importance of each feature in
classification. Additionally, we present the first astronomical use of
hierarchical classification methods to incorporate a known class taxonomy in
the classifier, which further reduces the catastrophic error rate to 7.8%.
Excluding low-amplitude sources, our overall error rate improves to 14%, with a
catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure
Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results
In this paper we describe a new method for EEG signal classification in which the classification of one subject’s EEG signals is based on features learnt from another subject. This method applies to the power spectrum density data and assigns class-dependent information weights to individual features. The informative features appear to be rather similar among different subjects, thus supporting the view that there are subject independent general brain patterns for the same mental task. Classification is done via clustering using the intelligent k-means algorithm with the most informative features from a different subject. We experimentally compare our method with others.</jats:p
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