- Y. Shi et al. (Eds.): ICCS 2007, Part I, LNCS 4487, pp. 1090 1097, 2007. Springer-Verlag Berlin Heidelberg 2007
- COMPLEX NETWORKS IN CLIMATE SCIENCE: PROGRESS, OPPORTUNITIES AND CHALLENGES
- Creating Ensembles of Classifiers Nitesh Chawla, Steven Eschrich, and Lawrence O. Hall
- Descriptive Analysis of the Global Climate System and Predictive Modeling for Uncertainty Reduction in Climate Projections using Complex Networks
- A Network-Based Approach to Understanding and Predicting Diseases
- Bagging-Like Effects for Decision Trees and Neural Nets in Protein Secondary Structure Prediction
- Knowledge Discovery from Sensor Data (Sensor-KDD) Ranga Raju Vatsavai
- Identifying and evaluating community structure in complex networks Karsten Steinhaeuser, Nitesh V. Chawla *
- Learning to Classify Data Streams with Imbalanced Class Distributions
- Troubleshooting Thousands of Jobs on Production Grids Using Data Mining Techniques
- Activity Mining in Open Source Software Daniel Mack
- Journal of Artificial Intelligence Research 23 (2005) 331 --366 Submitted 06/04; published 03/05 2005 AI Access Foundation. All rights reserved.
- C4.5 and Imbalanced Data sets: Investigating the effect of sampling method, probabilistic estimate, and decision tree structure
- Exploiting Thread-Level Parallelism to Build Decision Trees
- Referral Centres for Systemic Autoimmune Diseases, Fondazione
- Short Paper: Troubleshooting Distributed Systems via Data Mining David A. Cieslak, Douglas Thain, and Nitesh V. Chawla
- Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data
- Computing Information Gain in Data Streams Alec Pawling, Nitesh V. Chawla, and Amitabh Chaudhary
- Bagging Is A Small-Data-Set Phenomenon Nitesh Chawla
- DisNet: A Framework for Distributed Graph Computation
- A Parallel Decision Tree Builder for Mining Very Large Visualization Datasets
- Anomaly Detection in a Mobile Communication Network Alec Pawling, Nitesh V. Chawla, and Greg Madey
- Mining in a Mobile Environment Sean McRoskey
- Evaluating Probability Estimates from Decision Trees Nitesh V. Chawla and David A. Cieslak
- Modeling a Store's Product Space as a Social Network Troy Raeder, Nitesh V. Chawla
- Journal of Machine Learning Research 1 (2008) 1-4 Submitted 8/08; Published 10/00 Model Monitor (M2
- CIDU 2011: The NASA Conference on Intelligent Data Understanding October 20-21, 2011 Computer History Museum, Mountain View, CA, USA
- Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data
- Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery
- Detecting Communities in Time-evolving Proximity Networks Saurav Pandit
- Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets
- DATA MINING FOR IMBALANCED DATASETS: AN OVERVIEW
- Session S1J 0-7803-9077-6/05/$20.00 2005 IEEE October 19 22, 2005, Indianapolis, IN
- Random Subspaces And Subsampling For 2-D Face Recognition Nitesh V. Chawla and Kevin W. Bowyer
- Journal of Artificial Intelligence Research 16 (2002) 321357 Submitted 09/01; published 06/02 SMOTE: Synthetic Minority Over-sampling Technique
- Authentication Anomaly Detection: A Case Study On A Virtual Private Network
- Towards Learning-based Sensor Management Karsten Steinhaeuser, Nitesh V. Chawla and Christian Poellabauer
- Model Monitor User's Guide version 1.0 Troy Raeder
- Data Min Knowl Disc (2010) 20:388415 DOI 10.1007/s10618-009-0156-z
- A study in machine learning from imbalanced data for sentence boundary detection in speech
- Data Min Knowl Disc DOI 10.1007/s10618-008-0087-0
- Applying Learning Algorithms to Music Ryan N. Lichtenwalter1
- Evolutionary Ensemble Creation and Thinning Jared Sylvester and Nitesh V. Chawla, Member, IEEE
- Pricing Based Framework for Benefit Scoring Nitesh Chawla
- Community Detection in a Large Real-World Social Network
- Many Are Better Than One: Improving Probabilistic Estimates From Decision Trees
- DOI: 10.1542/peds.2009-2621 published online May 10, 2010;Pediatrics
- Generalization Methods in Bioinformatics Steven Eschrich
- New Perspectives and Methods in Link Prediction Ryan N. Lichtenwalter
- DisNet: A Framework for Distributed Graph Computation
- Combating Imbalance in Network Intrusion Datasets David A Cieslak, Nitesh V Chawla, Aaron Striegel
- Evolutionary Ensembles: Combining Learning Agents using Genetic Algorithms Jared Sylvester and Nitesh V. Chawla
- An Exploration of Climate Data Using Complex Networks Karsten Steinhaeuser
- Learning Rules from Distributed Data Lawrence O. Hall1
- SMOTEBoost: Improving Prediction of the Minority Class in Boosting
- Empirical Comparison of Correlation Measures and Pruning Levels in Complex Networks Representing
- Decision Tree Learning on Very Large Data Sets Lawrence O. Hall, Nitesh Chawla and Kevin W. Bowyer
- Editorial: Special Issue on Learning from Imbalanced Data Nitesh V. Chawla
- Comparing Predictive Power in Climate Data: Clustering Matters
- Multi-Relational Link Prediction in Heterogeneous Information Networks
- METHODOLOGY ARTICLE Open Access A statistical approach to finding overlooked
- Market Basket Analysis with Networks Troy Raeder, Nitesh V. Chawla
- An Exploration of Climate Data Using Complex Networks Karsten Steinhaeuser
- Exploring Disease Interactions Using Combined Gene and Phenotype Networks Darcy Davis
- Generating Diverse Ensembles to Counter the Problem of Class Imbalance
- A Robust Decision Tree Algorithm for Imbalanced Data Sets and Sanjay Chawla
- Knowledge Discovery from Sensor Data (SensorKDD) Olufemi A. Omitaomu
- Discovery of Climate Patterns with Complex Networks K. Steinhaeuser1,2,3
- D. Cieslak, N. Chawla, "A Framework for Monitoring Classifiers' Perfor-mance: When and Why Failure Occurs?", Knowledge and Information Sys-
- JOURNAL OF LATEX CLASS FILES, VOL. 1, NO. 11, NOVEMBER 2002 1 SVMs Modeling for Highly Imbalanced
- Scaling Up Classifiers to Cloud Computers Christopher Moretti
- Learning Decision Trees for Unbalanced Data David A. Cieslak and Nitesh V. Chawla
- Analyzing PETs on Imbalanced Datasets When Training and Testing Class Distributions Differ
- Is Modularity the Answer to Evaluating Community Structure in Networks? K. Steinhaeuser1
- Scalable Learning with Thread-Level Parallelism Karsten Steinhaeuser, Nitesh V. Chawla
- Detecting Fractures in Classifier Performance David A. Cieslak, Nitesh V. Chawla
- Anomaly Detection in a Mobile Communication Alec Pawling, Nitesh V. Chawla, and Greg Madey
- Estimating Query Result Sizes for Proxy Caching in Scientific Database Federations
- Resource Access Pattern Mining for Dynamic Energy Dinesh Rajan, Christian Poellabauer, Nitesh Chawla
- Information Gain, Correlation and Support Vector Machines
- Designing Multiple Classifier Systems for Face Recognition
- Classification and knowledge discovery in protein databases Predrag Radivojaca,1
- Lessons Learned from Feature Selection Competition Nitesh V. Chawla NITESH.CHAWLA@CIBC.CA
- Distributed Pasting of Small Votes N. V. Chawla1
- Combining Decision Trees Learned in Parallel Lawrence O. Hall, Nitesh Chawla and Kevin W. Bowyer
- DisNet: A Distributed Framework for Graph Computation -The Manual
- Distributed learning with bagging-like performance Nitesh V. Chawla a
- Consequences of Variability in Classifier Performance Estimates Troy Raeder, T. Ryan Hoens, Nitesh V. Chawla
- Ensembles in Face Recognition: Tackling the extremes of high dimensionality, temporality, and variance in data
- Is Objective Function the Silver Bullet? A Case Study of Community Detection Algorithms on Social Networks
- User Generated Content Consumption and Social Networking in Knowledge-Sharing OSNs
- Complex Networks as a Unified Framework for Descriptive Analysis and Predictive Modeling in Climate Science
- SECURITY AND COMMUNICATION NETWORKS Security Comm. Networks. 2008; 1:287299
- A Supervised Learning Approach to the Unsupervised Clustering of Genes Andrew Rider1,3,4
- Predicting Individual Disease Risk Based on Medical Darcy A. Davis
- A Supervised Learning Approach to the Ensemble Clustering of Genes Andrew K. Rider1,3,4
- Data Min Knowl Disc DOI 10.1007/s10618-011-0222-1
- Journal of Machine Learning Research 12 (2011) 2489-2492 Submitted 4/11; Revised 7/11; Published 8/11 LPmade: Link Prediction Made Easy
- Multivariate and multiscale dependence in the global climate system revealed through complex networks
- LPmade -The Manual Ryan N. Lichtenwalter
- Heuristic Updatable Weighted Random Subspaces for Non-stationary Environments T. Ryan Hoens
- Noname manuscript No. (will be inserted by the editor)
- Building Decision Trees for the Multi-class Imbalance Problem
- Noname manuscript No. (will be inserted by the editor)
- A Supervised Learning Approach to the Ensemble Clustering of Genes Andrew K. Rider1,3,4
