Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing classifiers for imbalanced training sets
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Data mining on multimedia data
Data mining on multimedia data
An approach to mining the multi-relational imbalanced database
Expert Systems with Applications: An International Journal
Classification of weld flaws with imbalanced class data
Expert Systems with Applications: An International Journal
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
On the use of surrounding neighbors for synthetic over-sampling of the minority class
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
GUEST EDITORIAL: Intelligent data analysis in medicine-Recent advances
Artificial Intelligence in Medicine
A data-driven approach to manage the length of stay for appendectomy patients
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Artificial Intelligence in Medicine
Customer churn prediction --a case study in retail banking
Proceedings of the 2010 conference on Data Mining for Business Applications
Exploring the performance of resampling strategies for the class imbalance problem
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
A hierarchical shrinking decision tree for imbalanced datasets
DNCOCO'06 Proceedings of the 5th WSEAS international conference on Data networks, communications and computers
Expert Systems with Applications: An International Journal
Computational Biology and Chemistry
Preprocessing unbalanced data using support vector machine
Decision Support Systems
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
A combined approach to tackle imbalanced data sets
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
Objective: An important problem that arises in hospitals is the monitoring and detection of nosocomial or hospital acquired infections (NIs). This paper describes a retrospective analysis of a prevalence survey of NIs done in the Geneva University Hospital. Our goal is to identify patients with one or more NIs on the basis of clinical and other data collected during the survey. Methods and material: Standard surveillance strategies are time-consuming and cannot be applied hospital-wide; alternative methods are required. In NI detection viewed as a classification task, the main difficulty resides in the significant imbalance between positive or infected (11%) and negative (89%) cases. To remedy class imbalance, we explore two distinct avenues: (1) a new resampling approach in which both oversampling of rare positives and undersampling of the noninfected majority rely on synthetic cases (prototypes) generated via class-specific subclustering, and (2) a support vector algorithm in which asymmetrical margins are tuned to improve recognition of rare positive cases. Results and conclusion: Experiments have shown both approaches to be effective for the NI detection problem. Our novel resampling strategies perform remarkably better than classical random resampling. However, they are outperformed by asymmetrical soft margin support vector machines which attained a sensitivity rate of 92%, significantly better than the highest sensitivity (87%) obtained via prototype-based resampling. g.