Classification is one of the most popular data mining tasks with a wide range of applications,
and lots of algorithms have been proposed to build accurate and scalable classifiers. Most of …

In recent years, there has been growing interest in multi-relational classification research
and application, which addresses the difficulties in dealing with large relation search space …

An important task in multi-relational data mining is link-based classification which takes
advantage of attributes of links and linked entities, to predict the class label. The relational …

In this paper we propose an extension of the naïve Bayes classification method to the multi-
relational setting. In this setting, training data are stored in several tables related by foreign …

Feature selection is an essential data processing step to remove irrelevant and redundant
attributes for shorter learning time, better accuracy, and better comprehensibility. A number …

Relational databases are the most popular repository for structured data, and is thus one of
the richest sources of knowledge in the world. In a relational database, multiple relations are …

Data mining applications require learning algorithms to have high predictive accuracy, scale
up to large datasets, and produce comprehensible outcomes. Naive Bayes classifier has …

We present the relational Bayesian classifier (RBC), a modification of the simple Bayesian
classifier (SBC) for relational data. There exist several Bayesian classifiers that learn …

One of the most popular techniques for multi-relational data mining is Inductive Logic
Programming (ILP). Given a set of positive and negative examples, an ILP system ideally …

Multirelational classification aims at discovering useful patterns across multiple inter-
connected tables (relations) in a relational database. Many traditional learning techniques …