Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Parametric and nonparametric linear mappings of multidimensional data
Pattern Recognition
Seeking knowledge in the deluge of facts
Fundamenta Informaticae - Special issue: intelligent information systems
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Multidimensional access methods
ACM Computing Surveys (CSUR)
Data mining: practical machine learning tools and techniques with Java implementations
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Data Mining and Knowledge Discovery: Making Sense Out of Data
IEEE Expert: Intelligent Systems and Their Applications
High dimensional feature reduction via projection pursuit
High dimensional feature reduction via projection pursuit
A selective sampling approach to active feature selection
Artificial Intelligence
The impact of feature extraction on the performance of a classifier: kNN, Naïve Bayes and C4.5
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Simultaneous sample and gene selection using t-score and approximate support vectors
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
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"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimensions. In this paper, different feature extraction (FE) techniques are analyzed as means of dimensionality reduction, and constructive induction with respect to the performance of Naïve Bayes classifier. When a data set contains a large number of instances, some sampling approach is applied to address the computational complexity of FE and classification processes. The main goal of this paper is to show the impact of sample reduction on the process of FE for supervised learning. In our study we analyzed the conventional PCA and two eigenvector-based approaches that take into account class information. The first class-conditional 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. The experiments are conducted on ten UCI data sets, using four different strategies to select samples: (1) random sampling, (2) stratified random sampling, (3) kd-tree based selective sampling, and (4) stratified sampling with kd-tree based selection. Our experiments show that if the sample size for FE model construction is small then it is important to take into account both class information and data distribution. Further, for supervised learning the nonparametric FE approach needs much less instances to produce a new representation space that result in the same or higher classification accuracy than the other FE approaches.