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Aims. We study the photospheric magnetic field of ∼2000 active regions over solar cycle 23 to search for parameters that may be indicative of energy build-up and its subsequent release as a solar flare in the corona. Methods. We extract three sets of parameters: (1) snapshots in space and time: total flux, magnetic gradients, and neutral lines; (2) evolution in time: flux evolution; and (3) structures at multiple size scales: wavelet analysis. This work combines standard pattern recognition and classification techniques via a relevance vector machine to determine (i.e., classify) whether a region is expected to flare (≥C1.0 according to GOES). We consider classification performance using all 38 extracted features and several feature subsets. Classification performance is quantified using both the true positive rate (the proportion of flares correctly predicted) and the true negative rate (the proportion of non-flares correctly classified). Additionally, we compute the true skill score which provides an equal weighting to true positive rate and true negative rate and the Heidke skill score to allow comparison to other flare forecasting work. Results. We obtain a true skill score of ∼0.5 for any predictive time window in the range 2 to 24 h, with a true positive rate of ∼0.8 and a true negative rate of ∼0.7. These values do not appear to depend on the predictive time window, although the Heidke skill score (<0.5) does. Features relating to snapshots of the distribution of magnetic gradients show the best predictive ability over all predictive time windows. Other gradient-related features and the instantaneous power at various wavelet scales also feature in the top five (of 38) ranked features in predictive power. It has always been clear that while the photospheric magnetic field governs the coronal non-potentiality (and hence likelihood of producing a solar flare), photospheric magnetic field information alone is not sufficient to determine this in a unique manner. Furthermore we are only measuring proxies of the magnetic energy build up. We are still lacking observational details on why energy is released at any particular point in time. We may have discovered the natural limit of the accuracy of flare predictions from these large scale studies.

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“…;Melgani & Bruzzone 2004;Cao & Tay 2003;Tong & Koller 2002;Hua & Sun 2001;Furey et al 2000;Chapelle et al 1999;Drucker et al …”

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An automated classification approach to ranking photospheric proxies of magnetic energy build-up

Al-Ghraibah

Boucheron

McAteer

2015

A&A

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“…Active learning has enjoyed wide use in machine learning, with impressive results in many areas of application, such as text classification, speech recognition, image and video classification, and medical imaging (Lewis & Gale 1994;Tong & Chang 2001;Tong & Koller 2002;Yan et al 2003;Liu 2004;Tur et al 2005). Begin with a training set L and testing set U.…”

Section: Active Learningmentioning

confidence: 99%

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Richards

Starr

Brink

et al. 2011

ApJ

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Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because a) standard assumptions for machine-learned model selection procedures break down and b) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting (IW), co-training (CT), and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and OGLE, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply active learning to classify variable stars in the ASAS survey, finding dramatic improvement in our agreement with the ACVS catalog, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

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“…Now active learning studies mainly focus on which strategies are used to select the next sample to be identified from unidentified sample sets. For example, a version space splitting-based active learning strategies are proposed in [5]. The authors of [6] proposed a closest hyperplane strategy based ASVM algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improved Probabilistic Active Support Vector Machine Based Remote Sensing Image Classification

Jiang

2013

AMM

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Most existing methods of active support vector machine (ASVM) focus on the samples nearby the current separating hyperplane, which ignore some support vector (SV) samples which are far form the separating hyperplane, also pay not attention on whether the current separating hyperplane is close to the optimal one. In this paper a new classification method of ASVM based on improved probability-calculation method is presented. It not only presents a new method of calculating probability, but also measures the degree of closeness of the current separating hyperplane to the actual separating hyperplane by a confidence factor. Experimental results show the superiority of our proposed method both in classification accuracy and computing cost.

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Cited by 34 publications

References 24 publications

An automated classification approach to ranking photospheric proxies of magnetic energy build-up

An automated classification approach to ranking photospheric proxies of magnetic energy build-up

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Improved Probabilistic Active Support Vector Machine Based Remote Sensing Image Classification

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