Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Induction of Decision Trees and Bayesian Classification Applied to Diagnosis of Sport Injuries
CBMS'97 Proceedings of the 10th conference on Computer based medical systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
BIBE '00 Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering
Gene Classification Using Expression Profiles: A Feasibility Study
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Comparison of Feature Selection Strategies for Hearing Impairments Diagnostics
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Machine learning in prognosis of the femoral neck fracture recovery
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
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Accurate diagnosis and classification is the key issue for the optimal treatment of cancer patients. Several studies demonstrate that cancer classification can be estimated with high accuracy, sensitivity and specificity from microarray-based gene expression profiling using artificial neural networks. In this paper, a comprehensive study was undertaken to investigate the capability of the probabilistic neural networks (PNN) associated with a feature selection method, a so-called signal-to-noise statistic, in the application of cancer classification. The signal-to-noise statistic, which represents the correlation with the class distinction, is used to select the marker genes and trim the dimension of data samples for the PNN. The experimental results show that the association of the probabilistic neural network with the signal-to-noise statistic can achieve superior classification results for two types of acute leukemias and five categories of embryonal tumors of central nervous system with satisfactory computation speed. Furthermore, the signal-to-noise statistic analysis provides candidate genes for future study in understanding the disease process and the identification of potential targets for therapeutic intervention.