Artikel
A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification
To solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection. The proposed paradigm consists of three main stages: categorization of high dimensional data, high-dimensionality-trait-driven feature extraction, and high-dimensionality-trait-driven classifier selection. In the first stage, according to the definition of high-dimensionality and the relationship between sample size and feature dimensions, the high-dimensionality traits of credit dataset are further categorized into two types: 100 < feature dimensions < sample size, and feature dimensions ≥ sample size. In the second stage, some typical feature extraction methods are tested regarding the two categories of high dimensionality. In the final stage, four types of classifiers are performed to evaluate credit risk considering different high-dimensionality traits. For the purpose of illustration and verification, credit classification experiments are performed on two publicly available credit risk datasets, and the results show that the proposed high-dimensionality-trait-driven learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark models listed in this study.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Financial Innovation ; ISSN: 2199-4730 ; Volume: 7 ; Year: 2021 ; Issue: 1 ; Pages: 1-20 ; Heidelberg: Springer
- Klassifikation
-
Management
- Thema
-
Classifier selection
Credit risk classification
Feature extraction
High dimensionality
Trait-driven learning paradigm
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Yu, Lean
Yu, Lihang
Yu, Kaitao
- Ereignis
-
Veröffentlichung
- (wer)
-
Springer
- (wo)
-
Heidelberg
- (wann)
-
2021
- DOI
-
doi:10.1186/s40854-021-00249-x
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Yu, Lean
- Yu, Lihang
- Yu, Kaitao
- Springer
Entstanden
- 2021
Ähnliche Objekte (12)
High-dimensionality in statistics and portfolio optimization
Portfolio selection in case of high dimensionality
Geometric structure of high-dimensional data and dimensionality reduction
The behavioral high risk paradigm in psychopathology
Semi-parametric estimation of elliptical distribution in case of high dimensionality
Automatic classification of high frequency signals
Classification Method Performance in High Dimensions
Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
A sparse grid based method for generative dimensionality reduction of high-dimensional data
Integration and classification of high-througput data
The performance of shrinkage estimator for stock portfolio selection in case of high dimensionality
New exactly tractable limits for correlated lattice fermions: high dimensionality and infinite range hopping
High-dimensionality in statistics and portfolio optimization
Portfolio selection in case of high dimensionality
Geometric structure of high-dimensional data and dimensionality reduction
The behavioral high risk paradigm in psychopathology
Semi-parametric estimation of elliptical distribution in case of high dimensionality
Automatic classification of high frequency signals
Classification Method Performance in High Dimensions
Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
A sparse grid based method for generative dimensionality reduction of high-dimensional data
Integration and classification of high-througput data
The performance of shrinkage estimator for stock portfolio selection in case of high dimensionality
New exactly tractable limits for correlated lattice fermions: high dimensionality and infinite range hopping
High-dimensionality in statistics and portfolio optimization
Portfolio selection in case of high dimensionality
Geometric structure of high-dimensional data and dimensionality reduction
The behavioral high risk paradigm in psychopathology
Semi-parametric estimation of elliptical distribution in case of high dimensionality
Automatic classification of high frequency signals
Classification Method Performance in High Dimensions
Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
A sparse grid based method for generative dimensionality reduction of high-dimensional data
Integration and classification of high-througput data
The performance of shrinkage estimator for stock portfolio selection in case of high dimensionality