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Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/(sici)1097-0304(199804)43:4<402::aid-ccd8>3.0.co;2-copenalex-snapshot
Garbage in, garbage out
inferredcites →https://doi.org/10.1002/art.1790110111openalex-snapshot
Qualitative methods in arthritis research: Sampling and data analysis
inferredcites →https://doi.org/10.1002/asi.21426openalex-snapshot
Power law distributions in information science: Making the case for logarithmic binning
inferredcites →https://doi.org/10.1002/sec.528openalex-snapshot
What you see predicts what you get—lightweight agent‐based malware detection
inferredcites →https://doi.org/10.1002/sec.630openalex-snapshot
Modeling tools for detecting DoS attacks in WSNs
inferredcites →https://doi.org/10.1002/smr.2417openalex-snapshot
On the analysis of power law distribution in software component sizes
inferredcites →https://doi.org/10.1002/wics.1306openalex-snapshot
Applying statistical thinking to ‘Big Data’ problems
inferredcites →https://doi.org/10.1002/widm.1289openalex-snapshot
Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data
inferredcites →https://doi.org/10.1007/978-3-319-18305-3openalex-snapshot
Machine Learning in Radiation Oncology
inferredcites →https://doi.org/10.1007/978-90-481-9171-0openalex-snapshot
Heavy-Tailed Distributions in Disaster Analysis
inferredcites →https://doi.org/10.1007/978-90-481-9171-0_1openalex-snapshot
Distributions of Characteristics of Natural Disasters: Data and Classification
inferredcites →https://doi.org/10.1007/s00521-021-06103-6openalex-snapshot
BenchMetrics: a systematic benchmarking method for binary classification performance metrics
inferredcites →https://doi.org/10.1016/j.cities.2012.03.001openalex-snapshot
Accessibility of cities in the digital economy
inferredcites →https://doi.org/10.1016/j.imu.2017.07.004openalex-snapshot
Feature selection model based on clustering and ranking in pipeline for microarray data
inferredcites →https://doi.org/10.1016/j.jisa.2019.06.008openalex-snapshot
Cognitive security: A comprehensive study of cognitive science in cybersecurity
inferredcites →https://doi.org/10.1016/j.molonc.2014.03.008openalex-snapshot
Garbage in, garbage out: A critical evaluation of strategies used for validation of immunohistochemical biomarkers
inferredcites →https://doi.org/10.1016/j.patcog.2005.01.019openalex-snapshot
Optimal robust classifiers
inferredcites →https://doi.org/10.1016/j.patcog.2018.07.023openalex-snapshot
Wild patterns: Ten years after the rise of adversarial machine learning
inferredcites →https://doi.org/10.1017/cbo9781139103671openalex-snapshot
Passages from the Life of a Philosopher
inferredcites →https://doi.org/10.1037/apl0000214openalex-snapshot
Not all nonnormal distributions are created equal: Improved theoretical and measurement precision.
inferredcites →https://doi.org/10.1038/s41559-021-01644-4openalex-snapshot
Ecological network complexity scales with area
inferredcites →https://doi.org/10.1049/iet-ifs.2013.0095openalex-snapshot
Analysis of Bayesian classification‐based approaches for Android malware detection
inferredcites →https://doi.org/10.1080/00107510500052444openalex-snapshot
Power laws, Pareto distributions and Zipf's law
inferredcites →https://doi.org/10.1080/09332480.1994.11882492openalex-snapshot
Garbage In, Garbage Out: Can Statisticians Quantify the Effects of Poor Data?
inferredcites →https://doi.org/10.1097/aog.0b013e3181f98300openalex-snapshot
Epidemiologic Research Using Administrative Databases
inferredcites →https://doi.org/10.1109/ares.2013.80openalex-snapshot
A Classifier of Malicious Android Applications
inferredcites →https://doi.org/10.1109/badgers.2014.7openalex-snapshot
ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors
inferredcites →https://doi.org/10.1109/ibigdelft.2018.8625275openalex-snapshot
New Techniques in Profiling Big Datasets for Machine Learning with a Concise Review of Android Mobile Malware Datasets
inferredcites →https://doi.org/10.1109/icadiwt.2014.6814679openalex-snapshot
Droid permission miner: Mining prominent permissions for Android malware analysis
inferredcites →https://doi.org/10.1109/ictai.2013.53openalex-snapshot
Machine Learning for Android Malware Detection Using Permission and API Calls
inferredcites →https://doi.org/10.1109/iri.2017.59openalex-snapshot
Comparison of Visual Datasets for Machine Learning
inferredcites →https://doi.org/10.1109/isdfs.2017.7916512openalex-snapshot
Clustering and visualization of mobile application permissions for end users and malware analysts
inferredcites →https://doi.org/10.1109/iv.2008.17openalex-snapshot
Quantifying and Comparing Features in High-Dimensional Datasets
inferredcites →https://doi.org/10.1109/tifs.2014.2353996openalex-snapshot
Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection
inferredcites →https://doi.org/10.1109/ubmk.2017.8093539openalex-snapshot
Binary classification performance measures/metrics: A comprehensive visualized roadmap to gain new insights
inferredcites →https://doi.org/10.1111/j.1420-9101.2009.01775.xopenalex-snapshot
The common patterns of nature
inferredcites →https://doi.org/10.1111/risa.12587openalex-snapshot
Of Disasters and Dragon Kings: A Statistical Analysis of Nuclear Power Incidents and Accidents
inferredcites →https://doi.org/10.1137/070710111openalex-snapshot
Power-Law Distributions in Empirical Data
inferredcites →https://doi.org/10.1145/2046707.2046779openalex-snapshot
Android permissions demystified
inferredcites →https://doi.org/10.1145/2382196.2382224openalex-snapshot
Using probabilistic generative models for ranking risks of Android apps
inferredcites →https://doi.org/10.1162/qss_a_00144openalex-snapshot
“Garbage in, garbage out” revisited: What do machine learning application papers report about human-labeled training data?
inferredcites →https://doi.org/10.1177/1833358318774357openalex-snapshot
Data quality: “Garbage in – garbage out”
inferredcites →https://doi.org/10.1184/r1/6586835.v1openalex-snapshot
Power-law distributions in empirical data
inferredcites →https://doi.org/10.1186/1687-4153-2013-10openalex-snapshot
Scientific knowledge is possible with small-sample classification
inferredcites →https://doi.org/10.1287/ited.3.2.64openalex-snapshot
Using Soccer Goals to Motivate the Poisson Process
inferredcites →https://doi.org/10.1515/9781400843138openalex-snapshot
The New Gilded Age: From Unequal Democracy
inferredcites →https://doi.org/10.1609/aaai.v33i01.33014536openalex-snapshot
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure
inferredcites →https://doi.org/10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2openalex-snapshot
Log-normal Distributions across the Sciences: Keys and Clues
inferredcites →https://doi.org/10.17350/hjse19030000221openalex-snapshot
Gaining New Insight into Machine-Learning Datasets via Multiple Binary-Feature Frequency Ranks with a Mobile Benign/Malware Apps Example
inferredcites →https://doi.org/10.18434/m32189openalex-snapshot
NIST/SEMATECH e-Handbook of Statistical Methods (NIST Handbook 151)
inferredcites →https://doi.org/10.18637/jss.v000.i00openalex-snapshot
A Web Application Software for Causal-based Machine Learning Discrimination Estimation
inferredcites →https://doi.org/10.18637/jss.v064.i02openalex-snapshot
Fitting Heavy Tailed Distributions: The poweRlaw Package
inferredcites →https://doi.org/10.1890/07-1288.1openalex-snapshot
ON ESTIMATING THE EXPONENT OF POWER-LAW FREQUENCY DISTRIBUTIONS
inferredcites →https://doi.org/10.2307/1912557openalex-snapshot
Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
inferredcites →https://doi.org/10.2807/1560-7917.es.2020.25.5.2000062openalex-snapshot
Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20–28 January 2020
inferredcites →https://doi.org/10.3390/s21041504openalex-snapshot
A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance
inferredcites →https://doi.org/10.6028/nist.ir.8280openalex-snapshot
Face recognition vendor test part 3:
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches
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SPEM: Self-adaptive Pooling Enhanced Attention Module for Image Recognition
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Direct optical combined measurement of bubble hydrodynamics in transition flow regime of a bubble column utilizing two in-line vision probes with distinct specifications
inferred← citeshttps://doi.org/10.1016/j.ecresq.2023.12.006openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.iecr.4c03245openalex-snapshot
Direct Optical Measurement for Particle Dynamics in Multiphase Reactors: Hardware Composition, Image Processing and Uncertainties
inferred← citeshttps://doi.org/10.1039/d2sc04056eopenalex-snapshot
Uncertainty quantification for predictions of atomistic neural networks
inferred← citeshttps://doi.org/10.2139/ssrn.5389934openalex-snapshot
Flow-Constrained Risk Management for Operational Technology Security: A Multi-Criteria Framework for Critical Infrastructure
inferred← citeshttps://doi.org/10.2139/ssrn.5392980openalex-snapshot
Flow-Constrained Risk Management for Operational Technology Security: A Multi-Criteria Framework for Critical Infrastructure
inferred← citeshttps://doi.org/10.23939/istcmtm2026.01.063openalex-snapshot
METHOD FOR FAST ANOMALY DETECTION IN IT SYSTEMS LOG RECORDS
inferred← citeshttps://doi.org/10.24072/pcjournal.331openalex-snapshot
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A Garbage Classification Method Based on a Small Convolution Neural Network
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Priorisation des enjeux de la biodiversité dans un contexte de planification territoriale
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