Neural computing: theory and practice
Neural computing: theory and practice
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Robust Classification for Imprecise Environments
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Improving Text Classification using Local Latent Semantic Indexing
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Applications of singular-value decomposition (SVD)
Mathematics and Computers in Simulation - Special issue: Applications of computer algebra in science, engineering, simulation and special software
Classification and knowledge discovery in protein databases
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Topological approaches to covering rough sets
Information Sciences: an International Journal
Granulation of a fuzzy set: Nonspecificity
Information Sciences: an International Journal
Granular computing and dual Galois connection
Information Sciences: an International Journal
The class imbalance problem: A systematic study
Intelligent Data Analysis
A multiview approach for intelligent data analysis based on data operators
Information Sciences: an International Journal
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning from imbalanced data in surveillance of nosocomial infection
Artificial Intelligence in Medicine
Learning classifiers from imbalanced data based on biased minimax probability machine
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Recursive information granulation: aggregation and interpretation issues
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
MDS: a novel method for class imbalance learning
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Getting insights from the voices of customers: Conversation mining at a contact center
Information Sciences: an International Journal
Handling Class Imbalance Problems via Weighted BP Algorithm
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Information Sciences: an International Journal
Information Sciences: an International Journal
Behavioral assessment of recoverable credit of retailer's customers
Information Sciences: an International Journal
Business intelligence for delinquency risk management via cox regression
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Expert Systems with Applications: An International Journal
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
A normal distribution-based over-sampling approach to imbalanced data classification
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Identifying the medical practice after total hip arthroplasty using an integrated hybrid approach
Computers in Biology and Medicine
Class distribution estimation based on the Hellinger distance
Information Sciences: an International Journal
Computers in Biology and Medicine
Fast dimension reduction for document classification based on imprecise spectrum analysis
Information Sciences: an International Journal
Computers in Biology and Medicine
Information Sciences: an International Journal
Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry
Information Sciences: an International Journal
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Recently, the class imbalance problem has attracted much attention from researchers in the field of data mining. When learning from imbalanced data in which most examples are labeled as one class and only few belong to another class, traditional data mining approaches do not have a good ability to predict the crucial minority instances. Unfortunately, many real world data sets like health examination, inspection, credit fraud detection, spam identification and text mining all are faced with this situation. In this study, we present a novel model called the ''Information Granulation Based Data Mining Approach'' to tackle this problem. The proposed methodology, which imitates the human ability to process information, acquires knowledge from Information Granules rather then from numerical data. This method also introduces a Latent Semantic Indexing based feature extraction tool by using Singular Value Decomposition, to dramatically reduce the data dimensions. In addition, several data sets from the UCI Machine Learning Repository are employed to demonstrate the effectiveness of our method. Experimental results show that our method can significantly increase the ability of classifying imbalanced data.