Implementing agglomerative hierarchic clustering algorithms for use in document retrieval
Information Processing and Management: an International Journal
C4.5: programs for machine learning
C4.5: programs for machine learning
Projections for efficient document clustering
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Data Mining and Knowledge Discovery
Improving Identification of Difficult Small Classes by Balancing Class Distribution
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Mining with rarity: a unifying framework
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
Exploratory Under-Sampling for Class-Imbalance Learning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Boosted Classification Trees and Class Probability/Quantile Estimation
The Journal of Machine Learning Research
The class imbalance problem: A systematic study
Intelligent Data Analysis
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
| Hi-index | 0.00 |
Imbalanced data sets contain an unequal distribution of data samples among the classes and pose a challenge to the learning algorithms as it becomes hard to learn the minority class concepts. Synthetic oversampling techniques address this problem by creating synthetic minority samples to balance the data set. However, most of these techniques may create wrong synthetic minority samples which fall inside majority regions. In this respect, this paper presents a novel Cluster Based Synthetic Oversampling (CBSO) algorithm. CBSO adopts its basic idea from existing synthetic oversampling techniques and incorporates unsupervised clustering in its synthetic data generation mechanism. CBSO ensures that synthetic samples created via this method always lie inside minority regions and thus, avoids any wrong synthetic sample creation. Simualtion analyses on some real world datasets show the effectiveness of CBSO showing improvements in various assesment metrics such as overall accuracy, F-measure, and G-mean.