Artikel
Robust linear classifier for unequal cost ratios of misclassification
This paper focuses on the robust classification procedures when the assumption of equal cost of misclassification is violated. A normal distribution based data set is generated using the Statistical Analysis System (SAS) version 9.1. Using Barlett's approximation to chi-square, the data set was found to be homogenous and was subjected to three linear classifiers namely: Maximum Likelihood Discriminant Function (MLDF), Fisher's linear Discriminant Function and Distance Based Discriminant Function. To Judge the performances of these procedures, the Apparent Error Rates for each procedure is obtained for different cost ratios 1:1, 1:2, 1:3, 1:4 and 1:5 and sample sizes 5:5, 10:10, 20:20, 30:30, and 50:50. The results shows that the three procedures are insensitive to cost ratio exceeding ratio 1:2 and that MLDF was observed as robust discriminant function among classification functions considered.
- Language
-
Englisch
- Bibliographic citation
-
Journal: CBN Journal of Applied Statistics ; ISSN: 2476-8472 ; Volume: 02 ; Year: 2011 ; Issue: 1 ; Pages: 51-59 ; Abuja: The Central Bank of Nigeria
- Classification
-
Wirtschaft
- Subject
-
Apparent Error Rates
Maximum Likelihood Discriminant Function
Distance Based Discriminant Function
Fisher's linear
- Event
-
Geistige Schöpfung
- (who)
-
Ariyo, Oludare S.
Adebanji, A. O.
- Event
-
Veröffentlichung
- (who)
-
The Central Bank of Nigeria
- (where)
-
Abuja
- (when)
-
2011
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Ariyo, Oludare S.
- Adebanji, A. O.
- The Central Bank of Nigeria
Time of origin
- 2011
Other Objects (12)
The Unequal Cost of Job Loss across Countries
Hybrid classifier : methods of data, knowledge, and classifier combination
Dynamic conditional bias-adjusted carry cost rate futures hedge ratios
Antizipative classifier systems
A Novel Example-Dependent Cost-Sensitive Stacking Classifier to Identify Tax Return Defaulters
Unequal Bequests
Unequal Bequests
Deterministic, Stochastic, and Robust Cost-Aware Scheduling
Pairwise Naive Bayes Classifier
Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
Photochemistry: Unequal equilibria
An unequal match
The Unequal Cost of Job Loss across Countries
Hybrid classifier : methods of data, knowledge, and classifier combination
Dynamic conditional bias-adjusted carry cost rate futures hedge ratios
Antizipative classifier systems
A Novel Example-Dependent Cost-Sensitive Stacking Classifier to Identify Tax Return Defaulters
Unequal Bequests
Unequal Bequests
Deterministic, Stochastic, and Robust Cost-Aware Scheduling
Pairwise Naive Bayes Classifier
Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
Photochemistry: Unequal equilibria
An unequal match
The Unequal Cost of Job Loss across Countries
Hybrid classifier : methods of data, knowledge, and classifier combination
Dynamic conditional bias-adjusted carry cost rate futures hedge ratios
Antizipative classifier systems
A Novel Example-Dependent Cost-Sensitive Stacking Classifier to Identify Tax Return Defaulters
Unequal Bequests
Unequal Bequests
Deterministic, Stochastic, and Robust Cost-Aware Scheduling
Pairwise Naive Bayes Classifier
Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
Photochemistry: Unequal equilibria