Many real-world problems involve the simultaneous optimization of several competing
objectives and constraints that are difficult, if not impossible, to solve without the aid of …

Evolutionary algorithms are a class of stochastic search methods that attempts to emulate
the biological process of evolution, incorporating concepts of selection, reproduction, and …

This paper presents new multi-objective evolutionary hybrid algorithms for the design of
Radial Basis Function Networks (RBFNs) for classification problems. The algorithms are …

This paper provides a short review of various association rule classifiers (ARC) that have
been developed over the past decade and the common structure behind most ARCs …

This paper presents a new multi-objective evolutionary hybrid algorithm for the design of
Radial Basis Function Networks (RBFNs) for classification problems. The algorithm …

Data mining is the knowledge discovery process which helps in extracting interesting
patterns from large amount of data. With the amount of data doubling every three years, data …

Over the last two decades, Associative Classifiers have shown competitive performance in
the task of predicting class labels. Along with the performance in accuracy, associative …

Associative classification is characterized by accurate models and high model generation
time. Most time is spent in extracting and postprocessing a large set of irrelevant rules, which …

Established associative classification algorithms have shown to be very effective in handling
categorical data such as text data. The learned model is a set of rules that are easy to …

Record linkage (RL) is the process of identifying and linking data that relates to the same
physical entity across multiple heterogeneous data sources. Deterministic linkage methods …