Estimation of incomplete values in heterogeneous attribute large datasets using discretized Bayesian max–min ant colony optimization
Sign Up to like & getrecommendations! Published in 2017 at "Knowledge and Information Systems"
DOI: 10.1007/s10115-017-1123-4
Abstract: The size of datasets is becoming larger nowadays and missing values in such datasets pose serious threat to data analysts. Although various techniques have been developed by researchers to handle missing values in different kinds… read more here.
Keywords: missing values; methodology; max min; min ant ... See more keywords
Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators
Sign Up to like & getrecommendations! Published in 2021 at "Asia-Pacific Financial Markets"
DOI: 10.1007/s10690-021-09341-9
Abstract: Stock return forecasting is of utmost importance in the business world. This has been a major topic of research for many academicians for decades. Recently, regularization techniques have reported significant increase in the forecast accuracy… read more here.
Keywords: important company; datasets using; using important; large datasets ... See more keywords
Mining Big Data with Random Forests
Sign Up to like & getrecommendations! Published in 2018 at "Cognitive Computation"
DOI: 10.1007/s12559-018-9615-4
Abstract: In the current big data era, naive implementations of well-known learning algorithms cannot efficiently and effectively deal with large datasets. Random forests (RFs) are a popular ensemble-based method for classification. RFs have been shown to… read more here.
Keywords: big data; mining big; random forests; large datasets ... See more keywords
Estimation of spatial econometric linear models with large datasets: How big can spatial Big Data be?
Sign Up to like & getrecommendations! Published in 2019 at "Regional Science and Urban Economics"
DOI: 10.1016/j.regsciurbeco.2019.01.006
Abstract: Abstract Spatial econometrics is currently experiencing the Big Data revolution both in terms of the volume of data and the velocity with which they are accumulated. Regional data, employed traditionally in spatial econometric modeling, can… read more here.
Keywords: large datasets; econometric linear; estimation spatial; spatial econometric ... See more keywords
Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations
Sign Up to like & getrecommendations! Published in 2019 at "Scientific Reports"
DOI: 10.1038/s41598-018-37524-4
Abstract: Shear-waves are the most energetic body-waves radiated from an earthquake, and are responsible for the destruction of engineered structures. In both short-term emergency response and long-term risk forecasting of disaster-resilient built environment, it is critical… read more here.
Keywords: ground shaking; wave radiation; radiation pattern; shear wave ... See more keywords
Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition
Sign Up to like & getrecommendations! Published in 2017 at "AIP Advances"
DOI: 10.1063/1.4996024
Abstract: In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition… read more here.
Keywords: large datasets; mode selection; dynamic mode; decomposition ... See more keywords
Fitting spatial regressions to large datasets using unilateral approximations
Sign Up to like & getrecommendations! Published in 2018 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2017.1301476
Abstract: ABSTRACT Maximum likelihood estimation of a spatial model typically requires a sizeable computational capacity, even in relatively small samples, and becomes unfeasible in very large datasets. The unilateral approximation approach to spatial model estimation (suggested… read more here.
Keywords: spatial regressions; using unilateral; regressions large; datasets using ... See more keywords
Identifying correlations driven by influential observations in large datasets
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbab482
Abstract: Although high-throughput data allow researchers to interrogate thousands of variables simultaneously, it can also introduce a significant number of spurious results. Here we demonstrate that correlation analysis of large datasets can yield numerous false positives… read more here.
Keywords: driven influential; observations large; large datasets; correlations driven ... See more keywords
qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets
Sign Up to like & getrecommendations! Published in 2020 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btaa637
Abstract: Abstract Motivation Non-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often… read more here.
Keywords: qsne quadratic; rate; large datasets; rate sne ... See more keywords
Detection and Classification of Anomalies in Large Datasets on the Basis of Information Granules
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Fuzzy Systems"
DOI: 10.1109/tfuzz.2021.3076265
Abstract: Anomaly (outlier) detection is one of the most important problems of modern data analysis. The sources of anomalies are varying. They can be the results of database users’ mistakes, operational errors, or just missing values.… read more here.
Keywords: detection classification; detection; large datasets; information granules ... See more keywords
Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets
Sign Up to like & getrecommendations! Published in 2017 at "Annals of the New York Academy of Sciences"
DOI: 10.1111/nyas.13272
Abstract: Access to experimental X‐ray diffraction image data is important for validation and reproduction of macromolecular models and indispensable for the development of structural biology processing methods. In response to the evolving needs of the structural… read more here.
Keywords: structural biology; system; data repository; biology ... See more keywords