Introduction to statistical pattern recognition (2nd ed.)
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Parametric and nonparametric linear mappings of multidimensional data
Pattern Recognition
C4.5: programs for machine learning
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Multiclass discriminant mappings
Signal Processing
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Fundamenta Informaticae - Special issue: intelligent information systems
Data mining: practical machine learning tools and techniques with Java implementations
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Database-friendly random projections
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Data Mining and Knowledge Discovery: Making Sense Out of Data
IEEE Expert: Intelligent Systems and Their Applications
Eigenvector-Based Feature Extraction for Classification
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
A comparative assessment of classification methods
Decision Support Systems
High dimensional feature reduction via projection pursuit
High dimensional feature reduction via projection pursuit
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The impact of sample reduction on PCA-based feature extraction for supervised learning
Proceedings of the 2006 ACM symposium on Applied computing
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“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and the classification error in high dimensions In this paper, different feature extraction techniques as means of (1) dimensionality reduction, and (2) constructive induction are analyzed with respect to the performance of a classifier Three commonly used classifiers are taken for the analysis: kNN, Naïve Bayes and C4.5 decision tree One of the main goals of this paper is to show the importance of the use of class information in feature extraction for classification and (in)appropriateness of random projection or conventional PCA to feature extraction for classification for some data sets Two eigenvector-based approaches that take into account the class information are analyzed The first approach is parametric and optimizes the ratio of between-class variance to the within-class variance of the transformed data The second approach is a nonparametric modification of the first one based on the local calculation of the between-class covariance matrix In experiments on benchmark data sets these two approaches are compared with each other, with conventional PCA, with random projection and with plain classification without feature extraction for each classifier.