International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Graphics gems
Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
Neural networks and the bias/variance dilemma
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Machine Learning
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Classification with Degree of Membership: A Fuzzy Approach
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Statistical Control of RBF-like Networks for Classification
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Comparisons of QP and LP Based Learning from Empirical Data
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Generalization Bounds for Decision Trees
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Genetic Programming for data classification: partitioning the search space
Proceedings of the 2004 ACM symposium on Applied computing
An Integer Support Vector Machine
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Projectable Classifiers of Arbitrary Complexity
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
A Novel Classification Algorithm Based on Fuzzy Kernel Multiple Hyperspheres
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Learning and classification with prime implicants applied to medical data diagnosis
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
Selecting and constructing features using grammatical evolution
Pattern Recognition Letters
Extended Naive Bayes classifier for mixed data
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A method for improving the accuracy of data mining classification algorithms
Computers and Operations Research
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Further experimental evidence against the utility of Occam's razor
Journal of Artificial Intelligence Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Effectiveness of fuzzy discretization for class association rule-based classification
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Prototype-based threshold rules
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
A hybrid analytical-heuristic method for calibrating land-use change models
Environmental Modelling & Software
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Current classification algorithms usually do not try to achieve a balance between fitting and generalization when they infer models from training data. Furthermore, current algorithms ignore the fact that there may be different penalty costs for the false-positive, false-negative, and unclassifiable types. Thus, their performance may not be optimal or may even be coincidental. This paper proposes a meta-heuristic approach, called the Convexity Based Algorithm (CBA), to address these issues. The new approach aims at optimally balancing the data fitting and generalization behaviors of models when some traditional classification approaches are used. The CBA first defines the total misclassification cost (TC) as a weighted function of the three penalty costs and the corresponding error rates as mentioned above. Next it partitions the training data into regions. This is done according to some convexity properties derivable from the training data and the traditional classification method to be used in conjunction with the CBA. Next the CBA uses a genetic approach to determine the optimal levels of fitting and generalization. The TC is used as the fitness function in this genetic approach. Twelve real-life datasets from a wide spectrum of domains were used to better understand the effectiveness of the proposed approach. The computational results indicate that the CBA may potentially fill in a critical gap in the use of current or future classification algorithms.