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Feature ranking and network analysis of global financial indices [PDF]
PLoS ONE, 2022 The feature ranking method of machine learning is applied to investigate the feature ranking and network properties of 21 world stock indices. The feature ranking is the probability of influence of each index on the target.
Mahmudul Islam Rakib +2 more
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Structure and dynamics of financial networks by feature ranking method [PDF]
Scientific Reports, 2021 Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks.
Mahmudul Islam Rakib +2 more
doaj +2 more sources
A feature selection method with feature ranking using genetic programming
Connection Science, 2022 Feature selection is a data processing method which aims to select effective feature subsets from original features. Feature selection based on evolutionary computation (EC) algorithms can often achieve better classification performance because of their ...
Guopeng Liu +3 more
doaj +2 more sources
Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu [PDF]
FoodsThe advantages of pottery jars in the aging process of Baijiu are evident, but the impact of their material composition and pore structure on the flavor of Baijiu has not been widely studied. This study systematically analyzed the effects of six types of
Haili Yang +5 more
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Insights into distributed feature ranking [PDF]
Information Sciences, 2019 Xunta de Galicia; ED431G ...
Konstantinos Sechidis +2 more
exaly +4 more sources
Feature Ranking on Small Samples: A Bayes-Based Approach [PDF]
EntropyIn the modern world, there is a need to provide a better understanding of the importance or relevance of the available descriptive features for predicting target attributes to solve the feature ranking problem.
Aleksandra Vatian +2 more
doaj +2 more sources
Feature ranking for multi-target regression [PDF]
Machine Learning, 2019 This paper considers multi-task regression (MTR) where the goal is to learn a model that predicts several target variables simultaneously. In particular the authors address the task of feature ranking to score the importance of descriptive attributes. While there is several work on feature ranking in single-task regression, this paper presents one of ...
Matej Petković +2 more
exaly +3 more sources
Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection
IEEE Access, 2019 Feature selection aims to eliminate redundant or irrelevant variables from input data to reduce computational cost, provide a better understanding of data and improve prediction accuracy.
Anwar Ul Haq +3 more
doaj +3 more sources
Bias and stability of single variable classifiers for feature ranking and selection [PDF]
Expert Systems With Applications, 2014 Shobeir Fakhraei +2 more
exaly +2 more sources
CoRR, 2023 Machine learning models are widely applied in various fields. Stakeholders often use post-hoc feature importance methods to better understand the input features' contribution to the models' predictions. The interpretation of the importance values provided by these methods is frequently based on the relative order of the features (their ranking) rather ...
Bitya Neuhof, Yuval Benjamini
openaire +3 more sources