Data Mining using Learning Classifier Systems

A Probabilistic Bayesian Classifier Approach for Breast Cancer Diagnosis and Prognosis

Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...

A Probabilistic Bayesian Classifier Approach for Breast Cancer Diagnosis and Prognosis

Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...

Enhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...

Supervised Learning Classifier Systems for Grid Data Mining

This paper explores parallel and distributed implementation of the Learning Classifier System (LCS) technology. Specifically, the adaptation of supervised LCS to the grid data mining requisites, using the agent paradigm, is studied. The paper also examines the competitive data mining model induction possibility with homogeneous and heterogeneous data. A distributed framework is proposed using t...

Learning Classifier Systems in Data Mining: An Introduction

Survey of Classifier Systems for Intrusion Detection System

This paper describes design for intrusion detection that combines anomaly detection with misuse detection. The proposed method includes an ensemble feature selecting classifier and a data mining classifier. The former consists of four classifiers using different sets of features and each of them employs a machine learning algorithm named fuzzy belief k-NN classification algorithm. The latter ap...