Evolutionary rule-based systems for imbalanced data sets

Authors:
Albert Orriols-Puig;Ester Bernadó-Mansilla
Affiliations:
Universitat Ramon Llull, Grup de Recerca en Sistemes Intelligents, Enginyeria i Arquitectura La Salle, Quatre Camins 2, 08022, Barcelona, Spain;Universitat Ramon Llull, Grup de Recerca en Sistemes Intelligents, Enginyeria i Arquitectura La Salle, Quatre Camins 2, 08022, Barcelona, Spain
Venue:
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
Year:
2008

Citing 0
Cited 40

Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets

International Journal of Approximate Reasoning
On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets

Expert Systems with Applications: An International Journal
Analysis and improvement of the genetic discovery component of XCS

International Journal of Hybrid Intelligent Systems - Data Mining and Hybrid Intelligent Systems
Can Evolution Strategies Improve Learning Guidance in XCS? Design and Comparison with Genetic Algorithms based XCS

Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
On the appropriateness of evolutionary rule learning algorithms for malware detection

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Performance evaluation of evolutionary algorithms in classification of biomedical datasets

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A Preliminar Analysis of CO2RBFN in Imbalanced Problems

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-Sets Using an Evolutionary Adaptive Inference System

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems

Applied Soft Computing
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning

IEEE Transactions on Evolutionary Computation
On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets

Information Sciences: an International Journal
Facetwise analysis of XCS for problems with class imbalances

IEEE Transactions on Evolutionary Computation
On the problems of using learning classifier systems for fraud detection

Proceedings of the 12th annual conference on Genetic and evolutionary computation
Analysing bioHEL using challenging boolean functions

Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
An investigation of real-valued accuracy-based learning classifier systems for electronic fraud detection

Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets

Pattern Recognition Letters
Multi-class imbalanced data-sets with linguistic fuzzy rule based classification systems based on pairwise learning

IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Obtaining optimal class distribution for decision trees: comparative analysis of CTC and C4.5

CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Genetics-based machine learning for rule induction: state of the art, taxonomy, and comparative study

IEEE Transactions on Evolutionary Computation
A dynamic over-sampling procedure based on sensitivity for multi-class problems

Pattern Recognition
XCSF with local deletion: preventing detrimental forgetting

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Learning classifier systems

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Addressing the classification with imbalanced data: open problems and new challenges on class distribution

HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Evolutionary-based selection of generalized instances for imbalanced classification

Knowledge-Based Systems
Activity recognition: an evolutionary ensembles approach

Proceedings of the 2011 international workshop on Situation activity & goal awareness
A genetic algorithm-based rule extraction system

Applied Soft Computing
C4.5 consolidation process: an alternative to intelligent oversampling methods in class imbalance problems

CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics

Expert Systems with Applications: An International Journal
Editorial: Large scale instance selection by means of federal instance selection

Data & Knowledge Engineering
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification

Pattern Recognition
A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets

Knowledge-Based Systems
EEM: evolutionary ensembles model for activity recognition in Smart Homes

Applied Intelligence
Strength-based learning classifier systems revisited: Effective rule evolution in supervised classification tasks

Engineering Applications of Artificial Intelligence
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches

Knowledge-Based Systems
Classifier ensemble optimization for human activity recognition in smart homes

Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Addressing imbalanced classification with instance generation techniques: IPADE-ID

Neurocomputing
On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed

Information Sciences: an International Journal
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems

Applied Soft Computing
EFM: evolutionary fuzzy model for dynamic activities recognition using a smartphone accelerometer

Applied Intelligence
A combined approach to tackle imbalanced data sets

International Journal of Hybrid Intelligent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper investigates the capabilities of evolutionary on-line rule-based systems, also called learning classifier systems (LCSs), for extracting knowledge from imbalanced data. While some learners may suffer from class imbalances and instances sparsely distributed around the feature space, we show that LCSs are flexible methods that can be adapted to detect such cases and find suitable models. Results on artificial data sets specifically designed for testing the capabilities of LCSs in imbalanced data show that LCSs are able to extract knowledge from highly imbalanced domains. When LCSs are used with real-world problems, they demonstrate to be one of the most robust methods compared with instance-based learners, decision trees, and support vector machines. Moreover, all the learners benefit from re-sampling techniques. Although there is not a re-sampling technique that performs best in all data sets and for all learners, those based in over-sampling seem to perform better on average. The paper adapts and analyzes LCSs for challenging imbalanced data sets and establishes the bases for further studying the combination of re-sampling technique and learner best suited to a specific kind of problem.