Results 31 to 40 of about 3,606,574 (279)
Robust nearest-neighbor methods for classifying high-dimensional data [PDF]
, 2009 We suggest a robust nearest-neighbor approach to classifying high-dimensional data. The method enhances sensitivity by employing a threshold and truncates to a sequence of zeros and ones in order to reduce the deleterious impact of heavy-tailed data ...
Chan, Yao-ban, Hall, Peter
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Program Evaluation and Causal Inference with High-Dimensional Data [PDF]
, 2016 In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables,
Belloni, Alexandre +3 more
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Frontiers in Cardiovascular Medicine, 2022 Growing evidence suggests a wide spectrum of potential cardiovascular complications following cancer therapies, leading to an urgent need for better risk-stratifying and disease screening in patients undergoing oncological treatment.
Haidee Chen +5 more
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Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data [PDF]
, 2015 Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research.
Liu, Lingqiao +4 more
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Journal of the National Science Foundation of Sri Lanka, 2016 This paper provides a brief introduction to high-dimensional data, a form of ‘Big Data’, and gives an overview of several data analysis concepts and techniques that could be used to explore and analyse such data. An example that involves genomics data from several Sri Lankan and United Kingdom oral cancer patients is used to illustrate the methods.
Dhammika Amaratunga, Javier Cabrera
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Finding k-Dominant G-Skyline Groups on High Dimensional Data
IEEE Access, 2018 Skyline query retrieves a set of skyline points which are not dominated by any other point and has attracted wide attention in database community. Recently, an important variant G-Skyline is developed. It aims to return optimal groups of points. However,
Kaiqi Zhang +3 more
doaj +1 more source
A classification method for high‐dimensional imbalanced multi‐classification data
Electronics Letters, 2023 High‐dimensional imbalanced multi‐classification problems (HDIMCPs) occur frequently in engineering applications such as medical detection, item classification, and email classification.
Mengmeng Li +5 more
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Consistent and Flexible Selectivity Estimation for High-dimensional Data
, 2020 Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion. Answering this problem accurately and efficiently is essential to many applications, such as density estimation, outlier detection, query ...
Ishikawa, Yoshiharu +7 more
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An efficient predictive analytics system for high dimensional big data
Journal of King Saud University: Computer and Information Sciences, 2022 The excessive growth of high dimensional big data has resulted in a greater challenge for data scientists to efficiently obtain valuable knowledge from these data. Traditional data mining techniques are not fit to process big data.
Myat Cho Mon Oo, Thandar Thein
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Asymptotic inference for high-dimensional data
, 2010 In this paper, we study inference for high-dimensional data characterized by small sample sizes relative to the dimension of the data. In particular, we provide an infinite-dimensional framework to study statistical models that involve situations in ...
Kuelbs, Jim, Vidyashankar, Anand N.
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