scholarly journals Online Learning and Decision-Making under Generalized Linear Model with High-Dimensional Data

2018 ◽  
Author(s):  
Xue Wang ◽  
Mike Mingcheng Wei ◽  
Tao Yao
Keyword(s):  
Decision Making ◽  
Online Learning ◽  
Linear Model ◽  
Generalized Linear Model ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Outlier Detection in High Dimensional Data

2021 ◽  
Vol 10 (5) ◽  
pp. 128-130
Author(s):  
Anusha L. ◽  
Nagaraja G S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Outlier Detection ◽  
Text Processing ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Human Intelligence ◽  
Over Time

Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

scholarly journals Multiattribute Fuzzy Decision Evaluation Approach and Its Application in Enterprise Competitiveness Evaluation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jing Li ◽  
Yue Sun ◽  
Lingling Gong ◽  
Nana Chai ◽  
Yanfei Yin
Keyword(s):  
Complex System ◽  
Decision Making ◽  
High Dimensional Data ◽  
Attribute Reduction ◽  
Academic Research ◽  
High Dimensional ◽  
System Evaluation ◽  
Consistency Test ◽  
Attribute Weights ◽  
Evaluation Approach

Multiattribute decision-making approach is one of the key complex system evaluation technologies which has attracted high attention of academic research studies. This paper establishes a novel multiattribute decision evaluation approach. First, we propose a high-dimensional data attribute reduction model based on partial correlation analysis and factor analysis methods. Second, based on the attribute weights calculated by multiple weighting methods, the corresponding multiple evaluation score vectors of the objects evaluated can be obtained. The final scoring vector can be determined by combining the quadratic combination weighting and the Spearman consistency test. Third, we use fuzzy C-means algorithm to grade evaluated objects. Finally, the established evaluation approach in this paper is verified by using the 107 observations in China. This approach also provides a decision-making example for attribute reduction of high-dimensional data, scoring of complex system evaluation, and clustering analysis when conducting evaluation in other fields.

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