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Quinlan, J. R.

doi:10.1023/a:1022643204877

Cited by 3,958 publications

(264 citation statements)

References 18 publications

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“…To understand how Random Forest classifiers operate, we must first describe their fundamental components: decision trees. A decision tree (Quinlan 1986) is a graph theory structure where nodes represent attributes and edges are the possible values the attribute can take. For example, one node may represent the "(g − i) color" attribute, with two edges pointing out from the node representing two possible values, for example, "≤0.5" and ">0.5".…”

Section: Random Forestmentioning

confidence: 99%

“…The main challenge, therefore, is to build a suitable decision tree for a particular task, in our case, for the automatic classification of quasars and stars. Technical details about the building (training) process of a decision tree are beyond the scope of this paper, but they can be found in Quinlan (1986).…”

Section: Random Forestmentioning

confidence: 99%

“…Our approach to automated quasar identification from only photometric data is based on the Random Forest algorithm (Breiman 2001), which is a machine-learning algorithm based on multiple decision trees (Quinlan 1986). Applying Random Forests in astronomy is relatively novel, dating back only a few years (e.g., Dubath et al 2011;Richards et al 2011;Pichara et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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Photometric classification of quasars from RCS-2 using Random Forest

Carrasco

Barrientos

Pichara

et al. 2015

A&A

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The classification and identification of quasars is fundamental to many astronomical research areas. Given the large volume of photometric survey data available in the near future, automated methods for doing so are required. In this article, we present a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested using a well-defined SDSS spectroscopic sample of quasars and stars. The Random Forest algorithm constructs the catalog from RCS-2 point sources using SDSS spectroscopicallyconfirmed stars and quasars. The algorithm identifies putative quasars from broadband magnitudes (g, r, i, z) and colors. Exploiting NUV GALEX measurements for a subset of the objects, we refine the classifier by adding new information. An additional subset of the data with WISE W1 and W2 bands is also studied. Upon analyzing 542 897 RCS-2 point sources, the algorithm identified 21 501 quasar candidates with a training-set-derived precision (the fraction of true positives within the group assigned quasar status) of 89.5% and recall (the fraction of true positives relative to all sources that actually are quasars) of 88.4%. These performance metrics improve for the GALEX subset: 6529 quasar candidates are identified from 16 898 sources, with a precision and recall of 97.0% and 97.5%, respectively. Algorithm performance is further improved when WISE data are included, with precision and recall increasing to 99.3% and 99.1%, respectively, for 21 834 quasar candidates from 242 902 sources. We compiled our final catalog (38 257) by merging these samples and removing duplicates. An observational follow up of 17 bright (r < 19) candidates with long-slit spectroscopy at DuPont telescope (LCO) yields 14 confirmed quasars. The results signal encouraging progress in the classification of point sources with Random Forest algorithms to search for quasars within current and future large-area photometric surveys.

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Section: Random Forestmentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Photometric classification of quasars from RCS-2 using Random Forest

Carrasco

Barrientos

Pichara

et al. 2015

A&A

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“…-the C5.0 classification-tree algorithm (Quinlan 1986(Quinlan , 1993 R package C50), -the k-nearest neighbor algorithm (KNN; see e.g. Altman 1992, R package class), and .…”

Section: Supervised Learningmentioning

confidence: 99%

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

Pasquato

Chung

2016

A&A

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Context. Machine-learning (ML) solves problems by learning patterns from data with limited or no human guidance. In astronomy, ML is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify globular clusters (GCs) that may have a history of merging from observational data. Methods. We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After carrying out dimensionality reduction on the feature space, the resulting datapoints are fed in to various classification algorithms. Using repeated random subsampling validation, we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations.Results. The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

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“…Decision tree learning algorithms have been in existence since the 1970s (see, e.g., Breiman et al 1984;Quinlan 1986). They are able to classify objects into discrete or continuous categories, scale well to large data sets, are fairly robust to inequitably sampled training sets, and can cope with values from individual objects that happen to be bad or irrelevant for the training targets being considered.…”

Section: Introductionmentioning

confidence: 99%

Robust Machine Learning Applied to Astronomical Data Sets. I. Star‐Galaxy Classification of the Sloan Digital Sky Survey DR3 Using Decision Trees

Ball

Brunner

Myers

et al. 2006

ApJ

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We provide classifications for all 143 million nonrepeat photometric objects in the Third Data Release of the SDSS using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r P 20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star, or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dFGRS, and 2QZ surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r $ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r $ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars.

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Untitled

Cited by 3,958 publications

References 18 publications

Photometric classification of quasars from RCS-2 using Random Forest

Photometric classification of quasars from RCS-2 using Random Forest

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

Robust Machine Learning Applied to Astronomical Data Sets. I. Star‐Galaxy Classification of the Sloan Digital Sky Survey DR3 Using Decision Trees

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