mmdt: multi-objective memetic rule learning from decision tree

MMDT: Multi-Objective Memetic Rule Learning from Decision Tree

In this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This...

Multi-objective Optimization for Incremental Decision Tree Learning

Decision tree learning can be roughly classified into two categories: static and incremental inductions. Static tree induction applies greedy search in splitting test for obtaining a global optimal model. Incremental tree induction constructs a decision model by analyzing data in short segments; during each segment a local optimal tree structure is formed. Very Fast Decision Tree [4] is a typic...

MMDT: a multi-valued and multi-labeled decision tree classifier for data mining

We have proposed a decision tree classifier named MMC (multi-valued and multi-labeled classifier) before. MMC is known as its capability of classifying a large multi-valued and multi-labeled data. Aiming to improve the accuracy of MMC, this paper has developed another classifier named MMDT (multi-valued and multi-labeled decision tree). MMDT differs from MMC mainly in attribute selection. MMC a...

Multi-Agent Decision Tree Learning from Distributed Autonomous Data Sources

Numeric Multi-Objective Rule Mining Using Simulated Annealing Algorithm

Abstract as a single objective one. Measures like support, confidence and other interestingness criteria which are used for evaluating a rule, can be thought of as different objectives of association rule mining problem. Support count is the number of records, which satisfies all the conditions that exist in the rule. This objective represents the accuracy of the rules extracted from the da...

Multi-Objective Decision Making

Many real-world tasks require making decisions that involve multiple possibly conflicting objectives. To succeed in such tasks, intelligent systems need planning or learning algorithms that can e ciently find di↵erent ways of balancing the trade-o↵s that such objectives present. In this tutorial, we provide an introduction to decision-theoretic approaches to coping with multiple objectives. We ...