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13 results for “Classification; Random Forest”
Datasets and results from: "Random Forest Classification and Solar Flares Data: Analysis and Validation"
<p><strong>Instructions for the data and code repository</strong></p> <p>Results, post-processing workflow, and datasets for the research paper titled "Random Forest Classification and Solar Flares Data: Analysis and Validation".</p> <p>The folder contains three .csv files: the complete dataset (dataset.csv), the balanced training dataset (train_dataset.csv), and the testing dataset (test_dataset.csv).</p> <p>The folder also contains the result files from the research (.csv output files with predictions and .html files with evaluation metrics, etc.) exported from the JASP software. The number in each file name corresponds to the number of trees utilized in Random Forest modelling.</p> <p>In addition, the Python script for the post-processing workflow is provided, with comments located in the script.</p> <p>The soft range X-ray irradiance and VLF amplitude data were obtained from:<br> National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed on: 24th June 2023. <br> Worldwide archive of low-frequency data and observations (WALDO) Available online: https://waldo.world/. Accessed on: 24th June 2023.</p>
Sentiment Analysis of RUU PDP with Naive Bayes, Support Vector Machine, and Random Forest Classification Algorithm
<p>Dataset from the results of data crawling via Twitter which discusses the Rancangan Undang Undang Pelindungan Data Pribadi to be used in the sentiment analysis process. The dataset is divided into several parts according to the process executed on RapidMiner.</p>
IIb-RAD-seq coupled with random forest classification indicates regional population structuring and sex-specific differentiation in salmon lice (Lepeophtheirus salmonis)
<p><span>The aquaculture industry has been dealing with salmon lice problems forming serious threats to salmonid farming. Several treatment approaches have been used to control the parasite. Treatment effectiveness must be optimized, and the systematic genetic differences between sub-populations must be studied to monitor louse species and enhance targeted control measures. We have used IIb-RAD sequencing in tandem with a random forest classification algorithm to detect the regional genetic structure of the Norwegian salmon lice and identify important markers for sex differentiation of this species. We identified 19428 single nucleotide polymorphisms (SNPs) from 95 individuals of salmon lice. These SNPs, however, were not able to distinguish differential structure of lice populations. Using the random forest algorithm, we selected 91 SNPs important for geographical classification and 14 SNPs important for sex classification. The geographically important SNP data substantially improved the genetic understanding of the population structure and classified regional demographic clusters along the Norwegian coast. </span><span>We also uncovered SNP markers that could help determine the sex of the salmon louse. </span><span>A large portion of the SNPs identified to be under directional selection were also ranked highly important by random forest. According to our findings, there is a regional population structure of salmon lice associated with the geographical location along the Norwegian coastline.</span></p>
Eurasian lynx GLCs' characteristics for classification with random forest algorithm
<p><span>Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires correct identification of kill sites, often achieved by field-checking GPS location clusters (GLCs). However, there are potential sources of error included in kill site identification, such as failing to detect GLCs that are kill sites and misclassifying the generated GLCs (e.g. kill for non-kill) that were not field-checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx, an apex predator of conservation concern in Europe, in three multi-prey systems, with different combinations of wild, semi-domestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affect the detection of GLCs indicating kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as non-kills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced to from 7 to 3 fixes/night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24-h decreased the probability of detecting GLCs connected with kill sites, particularly those of semi-domestic or domestic prey, and small prey. Random forest successfully predicted between 73%-90% of ungulate kills but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm's overall accuracy. We provide a set of recommendations for studies focusing on kill site detection, which can be considered for other large carnivore species besides the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.</span></p>
Trained Random Forest Model for PNW Seismic Event Classification Trained on 150s waveforms (P-50, P+100), 50 Hz, and 1-10 Hz BP Filtered
<p>This dataset contains three trained random forest models named as following - </p> <ul> <li>P_10_100_F_1_10_50.joblib - This is a model trained on 110s long waveforms (origin time - 10, origin time +100) in case of earthquakes and explosions and (first arrival pick -10, first arrival pick + 100) in case of surface events, the waveforms are tapered using 10% cosine taper, bandpass filtered between 1-10 Hz using Butterworth four corner filter, normalized and resampled to 50 Hz. </li> <li>P_50_100_F_1_10_50.joblib </li> <li>P_10_30_F_1_15_50.joblib. </li> </ul> <p>And also the standard scaler parameters for each features that will be used to normalize them. </p>
Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids
<p>Animal-attached devices can be used on cryptic species to measure their movement and behaviour, enabling unprecedented insights into fundamental aspects of animal ecology and behaviour. However, direct observations of subjects are often still necessary to translate biologging data accurately into meaningful behaviours. As many elusive species cannot easily be observed in the wild, captive or domestic surrogates are typically used to calibrate data from devices. However, the utility of this approach remains equivocal. </p> <p>Here, we assess the validity of using captive conspecifics, and phylogenetically-similar domesticated counterparts (surrogate species) for calibrating behaviour classification. Tri-axial accelerometers and tri-axial magnetometers were used with behavioural observations to build random forest models to predict the behaviours. We applied these methods using captive Alpine ibex (Capra ibex) and a domestic counterpart, pygmy goats (Capra aegagrus hircus), to predict the behaviour including terrain slope for locomotion behaviours of captive Alpine ibex. </p> <p>Behavioural classification of captive Alpine ibex and domestic pygmy goats was highly accurate (> 98%). Model performance was reduced when using data split per individual, i.e., classifying behaviour of individuals not used to train models (mean ± sd = 56.1 ± 11%). Behavioural classifications using domestic counterparts, i.e., pygmy goat observations to predict ibex behaviour, however, were not sufficient to predict all behaviours of a phylogenetically similar species accurately (> 55%).</p> <p>We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when using a domestic counterpart to predict the behaviour of a wild species in captivity; domestication leading to morphological differences and the terrain of the environment in which the animals were observed. We also identify limitations when behaviour is predicted in individuals that are not used to train models. Our results demonstrate that biologging device calibration needs to be conducted using: (i) with similar conspecifics, and (ii) in an area where they can perform behaviours on terrain that reflects that of species in the wild.</p>
Eurasian lynx GLCs' characteristics for classification with random forest algorithm
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Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids
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IIb-RAD-seq coupled with random forest classification indicates regional population structuring and sex-specific differentiation in salmon lice (Lepeophtheirus salmonis)
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Training Data For Random Forest Classification
<p>This is the training data for land use/cover classification and it was collected by doing visual interpretation from Google Earth Pro.</p>
Dataset for Ha and Aylward 'Automated classification of giant virus genomes using a random forest model built on trademark protein families'
<ul><li>Genome sets used for model training and testing</li><li>Custom Python script that generated fragmented genomes at random completeness levels</li></ul>
Presence of perennial halophilous scrubs in 2016 in the former saltworks of Salins de Giraud, random forest classification on WV2
<p>The presence of perennial halophilous scrubs (<em>S. fruticosa</em> and <em>A. macrostachyum</em>) was mapped using a Worldview 2 (WV2) very-high resolution imagery acquired the 31st of August 2016, strictly cloudless. Multispectral indices, among the most used, were adapted to the bands of WV2. The Random Forest model (RF) package into the R software was used to perform image classification. RF OBB and omission errors for the class “presence” and the class “absence” were less important when the classification was done for each species individually, hence this raster is a combination of both classifications. The OBB error for <em>S.fruticosa</em> is 1.43% and for <em>A. macrostachyum</em> is 2.18%.</p>
Dataset for "Random forest classification to predict response to high-definition transcranial direct current stimulation for tinnitus relief"
<p>This is the dataset necessary to reproduce the results described in the manuscript "Random forest classification to predict response to high-definition transcranial direct current stimulation for tinnitus relief".</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.