منابع مشابه
Autonomously evolving classifier TEDAClass
In this paper we introduce a classifier named TEDAClass (Typicality and Eccentricity based Data Analytics Classifier) which is based on the recently proposed AnYa type fuzzy rule based system. Specifically, the rules of the proposed classifier are defined according to the recently proposed TEDA framework. This novel and efficient systematic methodology for data analysis is a promising addition ...
متن کاملEvolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the ...
متن کاملA Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming
A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based fitness for individual fuzzy classifiers and employs evolution...
متن کاملClassifier systems evolving multi-agent system with distributed elitism
Classifier systems are rule-based control systems for the learning of more or less complex tasks. They evolve in an autonomous way through solution without any external help. The knowledge base (the population) consists of rule sets (the individuals) randomly generated. The population evolves due to the use of a genetic algorithm. Solving complex problems with classifier systems involves proble...
متن کاملA Hybrid Metaheuristic Framework for Evolving the PROAFTN Classifier
In this paper, a new learning algorithm based on a hybrid metaheuristic integrating Differential Evolution (DE) and Reduced Variable Neighborhood Search (RVNS) is introduced to train the classification method PROAFTN. To apply PROAFTN, values of several parameters need to be determined prior to classification. These parameters include boundaries of intervals and relative weights for each attrib...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Sciences
سال: 2016
ISSN: 0020-0255
DOI: 10.1016/j.ins.2016.05.012