Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems

Fuzzy Lattice Reasoning for Pattern Classification Using a New Positive Valuation Function

This paper describes an enhancement of fuzzy lattice reasoning (FLR) classifier for pattern classification based on a positive valuation function. Fuzzy lattice reasoning (FLR) was described lately as a lattice data domain extension of fuzzy ARTMAP neural classifier based on a lattice inclusion measure function. In this work, we improve the performance of FLR classifier by defining a new nonlinear positive valuation function. As a consequence, the modified algorithm achieves better classification results. The effectiveness of the modified FLR is demonstrated by examples on several well-known pattern recognition benchmarks

Multi-argument fuzzy measures on lattices of fuzzy sets

In this paper, we axiomatically introduce fuzzy multi-measures on bounded lattices. In particular, we make a distinction between four different types of fuzzy set multi-measures on a universe X, considering both the usual or inverse real number ordering of this lattice and increasing or decreasing monotonicity with respect to the number of arguments. We provide results from which we can derive families of measures that hold for the applicable conditions in each case

A comparison of machine learning techniques for detection of drug target articles

Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure.This research paper is supported by Projects TIN2007-67407- C03-01, S-0505/TIC-0267 and MICINN project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I + D + i), as well as for the Juan de la Cierva program of the MICINN of SpainPublicad

Induction of formal concepts by lattice computing techniques for tunable classification

This work proposes an enhancement of Formal Concept Analysis (FCA) by Lattice Computing (LC) techniques. More specifically, a novel Galois connection is introduced toward defining tunable metric distances as well as tunable inclusion measure functions between formal concepts induced from hybrid (i.e., nominal and numerical) data. An induction of formal concepts is pursued here by a novel extension of the Karnaugh map, or K-map for short, technique from digital electronics. In conclusion, granular classification can be pursued. The capacity of a classifier based on formal concepts is demonstrated here with promising results. The formal concepts are interpreted as descriptive decisionmaking knowledge (rules) induced from the training data

Data Mining Approach for Predicting Learner's Achievement

Student achievement variables that may be included into student database can be classified into three main categories, student variables. Instructor variables and general variables. This paper presents a new machine-learning model for extracting knowledge From student attributes in a given database. This knowledge can be used for determining the relative importance and effectiveness of student's attributes for the prediction of their college academic achievement, and the relationship between these attributes and their achievement. The model includes three main algorithms namely: preprocessing of database, attribute selection and rule extraction algorithm. Preprocessing of database aims to alleviate the dimensionality of the given database. It is performed according to (i) Detecting memo attributes and abstracting their field values into minimum abstraction level, (ii) Detecting the attributes, which have repeated values (including sparse values), and dropping them from database and (iii) Using fuzzification for transferring the attributes of continuous values into linguistic terms. This transformation leads to reducing the search space. Attribute selection algorithm selects the most relevant attributes set by the calculations of an evaluation function. The resulted set of attributes is passed to rule extraction algorithm for extracting an accurate and comprehensible set of rules.

Data Mining Approach for Predicting Learner's Achievement

Student achievement variables that may be included into student database can be classified into three main categories, student variables. Instructor variables and general variables. This paper presents a new machine-learning model for extracting knowledge From student attributes in a given database. This knowledge can be used for determining the relative importance and effectiveness of student's attributes for the prediction of their college academic achievement, and the relationship between these attributes and their achievement. The model includes three main algorithms namely: preprocessing of database, attribute selection and rule extraction algorithm. Preprocessing of database aims to alleviate the dimensionality of the given database. It is performed according to (i) Detecting memo attributes and abstracting their field values into minimum abstraction level, (ii) Detecting the attributes, which have repeated values (including sparse values), and dropping them from database and (iii) Using fuzzification for transferring the attributes of continuous values into linguistic terms. This transformation leads to reducing the search space. Attribute selection algorithm selects the most relevant attributes set by the calculations of an evaluation function. The resulted set of attributes is passed to rule extraction algorithm for extracting an accurate and comprehensible set of rules.