Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Computational limitations on learning from examples
Journal of the ACM (JACM)
Machine Learning
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Learning Logical Definitions from Relations
Machine Learning
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Parallel Genetic Algorithm for Concept Learning
Proceedings of the 6th International Conference on Genetic Algorithms
Centroid-Based Document Classification: Analysis and Experimental Results
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Constructing X-of-n Attributes With A Genetic Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Associative text categorization exploiting negated words
Proceedings of the 2006 ACM symposium on Applied computing
Advances in Evolutionary Algorithms: Theory, Design and Practice (Studies in Computational Intelligence)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
A Genetic Algorithm for Text Classification Rule Induction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Olex: Effective Rule Learning for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
A method for handling numerical attributes in GA-based inductive concept learners
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Improving the performance of a pittsburgh learning classifier system using a default rule
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
A genetic algorithms approach to ILP
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Classification inductive rule learning with negated features
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Extracting M-of-N rules from trained neural networks
IEEE Transactions on Neural Networks
A GA-based Learning Algorithm for Inducing M-of-N-like Text Classifiers
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 01
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While there has been a long history of rule-based text classifiers, to the best of our knowledge no M-of-N-based approach for text categorization has so far been proposed. In this paper we argue that M-of-N hypotheses are particularly suitable to model the text classification task because of the so-called ''family resemblance'' metaphor: ''the members (i.e., documents) of a family (i.e., category) share some small number of features, yet there is no common feature among all of them. Nevertheless, they resemble each other''. Starting from this conjecture, we provide a sound extension of the M-of-N approach with negation and disjunction, called M-of-N^{^@?^,^@?^}, which enables to best fit the true structure of the data. Based on a thorough theoretical study, we show that the M-of-N^{^@?^,^@?^} hypothesis space has two partial orders that form complete lattices. GAMoN is the task-specific Genetic Algorithm (GA) which, by exploiting the lattice-based structure of the hypothesis space, efficiently induces accurate M-of-N^{^@?^,^@?^} hypotheses. Benchmarking was performed over 13 real-world text data sets, by using four rule induction algorithms: two GAs, namely, BioHEL and OlexGA, and two non-evolutionary algorithms, namely, C4.5 and Ripper. Further, we included in our study linear SVM, as it is reported to be among the best methods for text categorization. Experimental results demonstrate that GAMoN delivers state-of-the-art classification performance, providing a good balance between accuracy and model complexity. Further, they show that GAMoN can scale up to large and realistic real-world domains better than both C4.5 and Ripper.