Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous versus exclusive classification for fingerprint retrieval
Pattern Recognition Letters
Fingerprint Classification by Directional Image Partitioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
FVC2000: Fingerprint Verification Competition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A pseudo-nearest-neighbor approach for missing data recovery on Gaussian random data sets
Pattern Recognition Letters
Fingerprint Indexing Based on Novel Features of Minutiae Triplets
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous fingerprints classification by symmetrical filters
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
A Novel Coding Scheme for Indexing Fingerprint Patterns
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Selection-fusion approach for classification of datasets with missing values
Pattern Recognition
Improvement of Fingerprint Retrieval by a Statistical Classifier
IEEE Transactions on Information Forensics and Security
Fingerprint Retrieval for Identification
IEEE Transactions on Information Forensics and Security
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Datasets with missing feature values are often encountered, especially in biometric databases. A common solution is to fill in the missing values by imputation. Unfortunately there is no universally best imputation method and the performance of a classifier can be degraded by poor imputations. In this paper, we propose a framework called the dynamic Fisher's linear discriminant that uses a quadratic classifier with a dynamically modified quadratic discriminant function. By eliminating imputations as far as possible, the proposed framework is useful for pattern classification. Satisfactory results are obtained from experiments conducted on four datasets from the UCI machine learning repository and the KEEL dataset repository, together with four fingerprint datasets.