Distribution-based aggregation for relational learning with identifier attributes
An Expected Utility Approach to Active Feature-Value Acquisition
ROC confidence bands: an empirical evaluation
Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification
Knowledge Discovery Using Concept-Class Taxonomies
Active Feature-Value Acquisition for Classifier Induction
Confidence Bands for ROC Curves: Methods and an Empirical Study
Active Sampling for Class Probability Estimation and Ranking
Aggregation-based feature invention and relational concept classes
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
Tree Induction vs. Logistic Regression: A Learning-Curve Analysis
Tree Induction for Probability-Based Ranking
Predicting citation rates for physics papers: constructing features for an ordered probit model
The myth of the double-blind review?: author identification using only citations
Active Learning for Class Probability Estimation and Ranking
Intelligent Information Triage
Applications of Data Mining to Electronic Commerce
Robust Classification for Imprecise Environments
Discovering Interesting Patterns for Investment Decision Making with GLOWER A Genetic Learner Overlaid with Entropy Reduction
Efficient Progressive Sampling
Activity Monitoring: Noticing Interesting Changes in Behavior
A Survey of Methods for Scaling Up Inductive Algorithms
Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case Study
Robust Classification Systems for Imprecise Environments
The Case against Accuracy Estimation for Comparing Induction Algorithms
AI Approaches to Fraud Detection and Risk Management
On Applied Research in Machine Learning
Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation
Scaling Up Inductive Algorithms: An Overview
Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions
Adaptive Fraud Detection
Scaling Up: Distributed Machine Learning with Cooperation
Exploiting Background Knowledge in Automated Discovery
Combining Data Mining and Machine Learning for Effective User Profiling
Scaling Up Inductive Learning with Massive Parallelism
Inductive Policy: The Pragmatics of Bias Selection
Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism
Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning
Iterative Weakening: Optimal and Near-Optimal Policies for the Selection of Search Bias
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network
Inductive Policy
Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search
Searching the Bias Space
A Distributed Algorithm for Embedding Trees in Hypercubes with Modifications for Run Time Fault Tolerance