Random Forest for improved analysis efficiency in passive acoustic monitoring

Ross, Jesse; Allen, Paul E.

doi:10.1016/j.ecoinf.2013.12.002

Cited by 40 publications

(21 citation statements)

References 10 publications

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“…The results shown above confirm that RF had the best performance, which is aligned with the results reported in most of the literature [11] [14]. Although one of RF's main advantages is that it does not require a fine-tuning process for its parameters [32], choosing the optimized values of parameters could protect the model from falling into overfitting.…”

Section: Tp / (Tp + Fn)supporting

confidence: 78%

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Al-Janabi

Quincey

András

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Abstract-The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.

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Section: Tp / (Tp + Fn)supporting

confidence: 78%

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Al-Janabi

Quincey

András

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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“…, Armitage & Ober , Kampichler et al . , Ross & Allen ). RF has produced consistently highly accurate results and is an algorithm that is easy to implement and has few computational requirements (Kampichler et al .…”

Section: Discussionmentioning

confidence: 97%

Classification success of six machine learning algorithms in radar ornithology

Rosa

Marques²,

Palminha³

et al. 2015

Ibis

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Radar systems have been increasingly used to monitor birds. To take full advantage of the large datasets provided by radars, researchers have implemented machine learning (ML) techniques that automatically read and attempt to classify targets. Here we used data collected from two locations in Portugal with two marine radar antennas (VSR and HSR) to apply and compare the performance of six ML algorithms that are widely used in the literature: random forests (RF), support vector machine (SVM), artificial neural networks (NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and decision trees (DT), all trained with several dataset configurations. We found that all algorithms performed well (area under the receiver operating characteristic (AUC) and accuracy > 0.80, P < 0.001) when discriminating birds from non-biological targets such as vehicles, rain or wind turbines, but greater variance in the performance among algorithms was apparent when separating different bird functional groups or bird species (e.g. herons vs. gulls). In our case study, only RF was able to hold an accuracy > 0.80 for all classification tasks, although SVM and DT also performed well. Further, all algorithms correctly classified 86% and 66% (VSR and HSR) of the target points, and only 2% and 4% of these points were misclassified by all algorithms. Our results suggest that ML algorithms are suitable for classifying radar targets as birds, and thereby separating them from other non-biological targets. The ability of these algorithms to correctly identify among bird species functional groups was found to be much weaker, but if properly trained and supported by a good ground truthing dataset, targeted to the relevant species groups, some of these algorithms are still able to achieve high accuracies in classification tasks. Such results indicate that ML algorithms are suitable for use in near real-time monitoring of bird movements, and may help to mitigate collision of birds with, for example, wind turbines or airplanes.

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“…Firstly, each loud sound in the recording will be extracted, even those that are not biophonic, and secondly, quiet calls will be ignored since they do not rise sufficiently far above the noise level. Therefore it remains subject to high false positives, unless it is made very specific to a particular species (Ganchev et al, ; Kahl, Wilhelm‐Stein, Klinck, Kowerko, & Eibl, ; de Oliveira et al, ; Ross & Allen, ). Finally, the recorded soundscape often combines a small number of species of interest together with more common species, and segmenting all of the sounds individually is inefficient.…”

Section: Introductionmentioning

confidence: 99%

Wavelet filters for automated recognition of birdsong in long‐time field recordings

et al. 2020

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Ecoacoustics has the potential to provide a large amount of information about the abundance of many animal species at a relatively low cost. Acoustic recording units are widely used in field data collection, but the facilities to reliably process the data recorded – recognizing calls that are relatively infrequent, and often significantly degraded by noise and distance to the microphone – are not well‐developed yet. We propose a call detection method for continuous field recordings that can be trained quickly and easily on new species, and degrades gracefully with increased noise or distance from the microphone. The method is based on the reconstruction of the sound from a subset of the wavelet nodes (elements in the wavelet packet decomposition tree). It is intended as a preprocessing filter, therefore we aim to minimize false negatives: false positives can be removed in subsequent processing, but missed calls will not be looked at again. We compare our method to standard call detection methods, and also to machine learning methods (using as input features either wavelet energies or Mel‐Frequency Cepstral Coefficients) on real‐world noisy field recordings of six bird species. The results show that our method has higher recall (proportion detected) than the alternative methods: 87% with 85% specificity on >53 hr of test data, resulting in an 80% reduction in the amount of data that needed further verification. It detected >60% of calls that were extremely faint (far away), even with high background noise. This preprocessing method is available in our AviaNZ bioacoustic analysis program and enables the user to significantly reduce the amount of subsequent processing required (whether manual or automatic) to analyse continuous field recordings collected by spatially and temporally large‐scale monitoring of animal species. It can be trained to recognize new species without difficulty, and if several species are sought simultaneously, filters can be run in parallel.

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Random Forest for improved analysis efficiency in passive acoustic monitoring

Cited by 40 publications

References 10 publications

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Classification success of six machine learning algorithms in radar ornithology

Wavelet filters for automated recognition of birdsong in long‐time field recordings

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