An empirical comparison of pattern recognition, neural nets, and machine learning classification methods

Authors:
Sholom M. Weiss;Ioannis Kapouleas
Affiliations:
Department of Computer Science, Rutgers University, New Brunswick, NJ;Department of Computer Science, Rutgers University, New Brunswick, NJ
Venue:
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Year:
1989

Citing 13
Cited 24

A practical guide to designing expert systems

A practical guide to designing expert systems
Heuristic classification

Artificial Intelligence
Classification algorithms

Classification algorithms
Toward memory-based reasoning

Communications of the ACM - Special issue on parallelism
Bootstrap Techniques for Error Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence
A pattern classification approach to evaluation function learning

Artificial Intelligence
Simplifying decision trees

International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Explorations in parallel distributed processing: a handbook of models, programs, and exercises

Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework

Artificial Intelligence
Extensions to the CART algorithm

International Journal of Man-Machine Studies
What size net gives valid generalization?

Neural Computation
Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Induction of Decision Trees

Machine Learning

The representation of importance and interaction of features by fuzzy measures

Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey

Data Mining and Knowledge Discovery
Feature Selection for Meta-learning

PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Incremental training of support vector machines using hyperspheres

Pattern Recognition Letters
MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students

Applied Soft Computing
ANLAGIS: Adaptive Neuron-Like Network Based on Learning Automata Theory and Granular Inference Systems with Applications to Pattern Recognition and Machine Learning

IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Divide and Conquer Neural Networks

Neural Networks
The Naive Bayes Mystery: A classification detective story

Pattern Recognition Letters
Pattern classification by multi-layer perceptron using fuzzy integral-based activation function

Applied Soft Computing
A meta-heuristic approach for improving the accuracy in some classification algorithms

Computers and Operations Research
On performance of case-based reasoning in Chinese business failure prediction from sensitivity, specificity, positive and negative values

Applied Soft Computing
Myths and legends in learning classification rules

AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
A hybrid connectionist, symbolic learning system

AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Rule learning by searching on adapted nets

AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
OC1: randomized induction of oblique decision trees

AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Detecting hedge cues and their scope in biomedical text with conditional random fields

Journal of Biomedical Informatics
A unified approach to the extraction of rules from artificial neural networks and support vector machines

ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning

Neurocomputing
A hybrid nonlinear classifier based on generalized choquet integrals

CASDMKM'04 Proceedings of the 2004 Chinese academy of sciences conference on Data Mining and Knowledge Management
Automatic recommendation of classification algorithms based on data set characteristics

Pattern Recognition
Fast training of linear programming support vector machines using decomposition techniques

ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Model of a sensory-behavioral relation mechanism for aggressive behavior in crickets

Robotics and Autonomous Systems
HEp-2 cell images classification based on textural and statistic features using self-organizing map

ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previously analyzed and reported in the statistical, medical, or machine learning literature. The data sets are characterized by statisucal uncertainty; there is no completely accurate solution to these problems. Training and testing or resampling techniques are used to estimate the true error rates of the classification methods. Detailed attention is given to the analysis of performance of the neural nets using back propagation. For these problems, which have relatively few hypotheses and features, the machine learning procedures for rule induction or tree induction clearly performed best.