Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks

Multitask Learning is a learning paradigm that deals with multiple different tasks in parallel and transfers knowledge among them. XOF, a Learning Classifier System using tree-based programs to encode building blocks (meta-features), constructs and collects features with rich discriminative information for classification tasks in an observed list. This paper seeks to facilitate the automation of feature transferring in between tasks by utilising the observed list. We hypothesise that the best discriminative features of a classification task carry its characteristics. Therefore, the relatedness between any two tasks can be estimated by comparing their most appropriate patterns. We propose a multiple-XOF system, called mXOF, that can dynamically adapt feature transfer among XOFs. This system utilises the observed list to estimate the task relatedness. This method enables the automation of transferring features. In terms of knowledge discovery, the resemblance estimation provides insightful relations among multiple data. We experimented mXOF on various scenarios, e.g. representative Hierarchical Boolean problems, classification of distinct classes in the UCI Zoo dataset, and unrelated tasks, to validate its abilities of automatic knowledge-transfer and estimating task relatedness. Results show that mXOF can estimate the relatedness reasonably between multiple tasks to aid the learning performance with the dynamic feature transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference (GECCO 2020

Medical data mining using evolutionary computation.

by Ngan Po Shun.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 109-115).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining --- p.1Chapter 1.2 --- Motivation --- p.4Chapter 1.3 --- Contributions of the research --- p.5Chapter 1.4 --- Organization of the thesis --- p.6Chapter 2 --- Related Work in Data Mining --- p.9Chapter 2.1 --- Decision Tree Approach --- p.9Chapter 2.1.1 --- ID3 --- p.10Chapter 2.1.2 --- C4.5 --- p.11Chapter 2.2 --- Classification Rule Learning --- p.13Chapter 2.2.1 --- AQ algorithm --- p.13Chapter 2.2.2 --- CN2 --- p.14Chapter 2.2.3 --- C4.5RULES --- p.16Chapter 2.3 --- Association Rule Mining --- p.16Chapter 2.3.1 --- Apriori --- p.17Chapter 2.3.2 --- Quantitative Association Rule Mining --- p.18Chapter 2.4 --- Statistical Approach --- p.19Chapter 2.4.1 --- Chi Square Test and Bayesian Classifier --- p.19Chapter 2.4.2 --- FORTY-NINER --- p.21Chapter 2.4.3 --- EXPLORA --- p.22Chapter 2.5 --- Bayesian Network Learning --- p.23Chapter 2.5.1 --- Learning Bayesian Networks using the Minimum Descrip- tion Length (MDL) Principle --- p.24Chapter 2.5.2 --- Discretizating Continuous Attributes while Learning Bayesian Networks --- p.26Chapter 3 --- Overview of Evolutionary Computation --- p.29Chapter 3.1 --- Evolutionary Computation --- p.29Chapter 3.1.1 --- Genetic Algorithm --- p.30Chapter 3.1.2 --- Genetic Programming --- p.32Chapter 3.1.3 --- Evolutionary Programming --- p.34Chapter 3.1.4 --- Evolution Strategy --- p.37Chapter 3.1.5 --- Selection Methods --- p.38Chapter 3.2 --- Generic Genetic Programming --- p.39Chapter 3.3 --- Data mining using Evolutionary Computation --- p.43Chapter 4 --- Applying Generic Genetic Programming for Rule Learning --- p.45Chapter 4.1 --- Grammar --- p.46Chapter 4.2 --- Population Creation --- p.49Chapter 4.3 --- Genetic Operators --- p.50Chapter 4.4 --- Evaluation of Rules --- p.52Chapter 5 --- Learning Multiple Rules from Data --- p.56Chapter 5.1 --- Previous approaches --- p.57Chapter 5.1.1 --- Preselection --- p.57Chapter 5.1.2 --- Crowding --- p.57Chapter 5.1.3 --- Deterministic Crowding --- p.58Chapter 5.1.4 --- Fitness sharing --- p.58Chapter 5.2 --- Token Competition --- p.59Chapter 5.3 --- The Complete Rule Learning Approach --- p.61Chapter 5.4 --- Experiments with Machine Learning Databases --- p.64Chapter 5.4.1 --- Experimental results on the Iris Plant Database --- p.65Chapter 5.4.2 --- Experimental results on the Monk Database --- p.67Chapter 6 --- Bayesian Network Learning --- p.72Chapter 6.1 --- The MDLEP Learning Approach --- p.73Chapter 6.2 --- Learning of Discretization Policy by Genetic Algorithm --- p.74Chapter 6.2.1 --- Individual Representation --- p.76Chapter 6.2.2 --- Genetic Operators --- p.78Chapter 6.3 --- Experimental Results --- p.79Chapter 6.3.1 --- Experiment 1 --- p.80Chapter 6.3.2 --- Experiment 2 --- p.82Chapter 6.3.3 --- Experiment 3 --- p.83Chapter 6.3.4 --- Comparison between the GA approach and the greedy ap- proach --- p.91Chapter 7 --- Medical Data Mining System --- p.93Chapter 7.1 --- A Case Study on the Fracture Database --- p.95Chapter 7.1.1 --- Results of Causality and Structure Analysis --- p.95Chapter 7.1.2 --- Results of Rule Learning --- p.97Chapter 7.2 --- A Case Study on the Scoliosis Database --- p.100Chapter 7.2.1 --- Results of Causality and Structure Analysis --- p.100Chapter 7.2.2 --- Results of Rule Learning --- p.102Chapter 8 --- Conclusion and Future Work --- p.106Bibliography --- p.109Chapter A --- The Rule Sets Discovered --- p.116Chapter A.1 --- The Best Rule Set Learned from the Iris Database --- p.116Chapter A.2 --- The Best Rule Set Learned from the Monk Database --- p.116Chapter A.2.1 --- Monkl --- p.116Chapter A.2.2 --- Monk2 --- p.117Chapter A.2.3 --- Monk3 --- p.119Chapter A.3 --- The Best Rule Set Learned from the Fracture Database --- p.120Chapter A.3.1 --- Type I Rules: About Diagnosis --- p.120Chapter A.3.2 --- Type II Rules : About Operation/Surgeon --- p.120Chapter A.3.3 --- Type III Rules : About Stay --- p.122Chapter A.4 --- The Best Rule Set Learned from the Scoliosis Database --- p.123Chapter A.4.1 --- Rules for Classification --- p.123Chapter A.4.2 --- Rules for Treatment --- p.126Chapter B --- The Grammar used for the fracture and Scoliosis databases --- p.128Chapter B.1 --- The grammar for the fracture database --- p.128Chapter B.2 --- The grammar for the Scoliosis database --- p.12