Randomization tests
C4.5: programs for machine learning
C4.5: programs for machine learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiple Comparisons in Induction Algorithms
Machine Learning
Relational Data Mining
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multi-relational Decision Tree Induction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A Machine Learning Approach to Building Domain-Specific Search Engines
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Stochastic Propositionalization of Non-determinate Background Knowledge
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Exploiting relational structure to understand publication patterns in high-energy physics
ACM SIGKDD Explorations Newsletter
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Leveraging relational autocorrelation with latent group models
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Leveraging Relational Autocorrelation with Latent Group Models
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficient Classification across Multiple Database Relations: A CrossMine Approach
IEEE Transactions on Knowledge and Data Engineering
Detecting outliers using transduction and statistical testing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Contextual Dependency Network Models for Link-Based Classification
IEEE Transactions on Knowledge and Data Engineering
Sequential inference with reliable observations: learning to construct force-dynamic models
Artificial Intelligence
Relational Dependency Networks
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Relational data pre-processing techniques for improved securities fraud detection
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting time-varying relationships in statistical relational models
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Applying link-based classification to label blogs
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A bias/variance decomposition for models using collective inference
Machine Learning
First-Order Probabilistic Languages: Into the Unknown
Inductive Logic Programming
ReMauve: A Relational Model Tree Learner
Inductive Logic Programming
A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Feature Discovery with Type Extension Trees
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Multirelational classification: a multiple view approach
Knowledge and Information Systems
Applying Link-Based Classification to Label Blogs
Advances in Web Mining and Web Usage Analysis
Learning to Extract Relations for Relational Classification
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
ILP-based concept discovery in multi-relational data mining
Expert Systems with Applications: An International Journal
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2005 conference on Multi-Relational Data Mining
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Learning first-order probabilistic models with combining rules
Annals of Mathematics and Artificial Intelligence
Learning models of macrobehavior in complex adaptive systems
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A relational representation for procedural task knowledge
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Structure learning for statistical relational models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Improving learning in networked data by combining explicit and mined links
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Online learning and exploiting relational models in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
View learning for statistical relational learning: with an application to mammography
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Sequential inference with reliable observations: Learning to construct force-dynamic models
Artificial Intelligence
Exploring optimization of semantic relationship graph for multi-relational Bayesian classification
Decision Support Systems
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
Cautious Collective Classification
The Journal of Machine Learning Research
Adding data mining support to SPARQL via statistical relational learning methods
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Semantic web enabled software analysis
Web Semantics: Science, Services and Agents on the World Wide Web
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Graph regularized transductive classification on heterogeneous information networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Permutation testing improves Bayesian network learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Discovering missing values in semi-structured databases
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Modeling the evolution of discussion topics and communication to improve relational classification
Proceedings of the First Workshop on Social Media Analytics
Application and evaluation of inductive reasoning methods for the semantic web and software analysis
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
A nonparametric classification method based on K-associated graphs
Information Sciences: an International Journal
Boosting tuple propagation in multi-relational classification
Proceedings of the 15th Symposium on International Database Engineering & Applications
Refining aggregate conditions in relational learning
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Robust collective classification with contextual dependency network models
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Using trees to mine multirelational databases
Data Mining and Knowledge Discovery
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
A comparison of approaches for learning probability trees
ECML'05 Proceedings of the 16th European conference on Machine Learning
CrossMine: efficient classification across multiple database relations
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
Time-Evolving relational classification and ensemble methods
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Link prediction in complex networks based on cluster information
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
Enhanced spatiotemporal relational probability trees and forests
Data Mining and Knowledge Discovery
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Simple decision forests for multi-relational classification
Decision Support Systems
Reducing the size of databases for multirelational classification: a subgraph-based approach
Journal of Intelligent Information Systems
Multi-label relational neighbor classification using social context features
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Type Extension Trees for feature construction and learning in relational domains
Artificial Intelligence
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Classification trees are widely used in the machine learning and data mining communities for modeling propositional data. Recent work has extended this basic paradigm to probability estimation trees. Traditional tree learning algorithms assume that instances in the training data are homogenous and independently distributed. Relational probability trees (RPTs) extend standard probability estimation trees to a relational setting in which data instances are heterogeneous and interdependent. Our algorithm for learning the structure and parameters of an RPT searches over a space of relational features that use aggregation functions (e.g. AVERAGE, MODE, COUNT) to dynamically propositionalize relational data and create binary splits within the RPT. Previous work has identified a number of statistical biases due to characteristics of relational data such as autocorrelation and degree disparity. The RPT algorithm uses a novel form of randomization test to adjust for these biases. On a variety of relational learning tasks, RPTs built using randomization tests are significantly smaller than other models and achieve equivalent, or better, performance.