EVOLVING TEXT CLASSIFICATION RULES WITH GENETIC PROGRAMMING
نویسندگان
چکیده
منابع مشابه
Evolving Text Classification Rules with Genetic Programming
We describe a novel method for using Genetic Programming to create compact classification rules using combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classifica...
متن کاملEvolving Text Classifiers with Genetic Programming
We describe a method for using Genetic Programming (GP) to evolve document classifiers. GP’s create regular expression type specifications consisting of particular sequences and patterns of N-Grams (character strings) and acquire fitness by producing expressions, which match documents in a particular category but do not match documents in any other category. Libraries of N-Gram patterns have be...
متن کاملDiscovering interesting classification rules with genetic programming
Data mining deals with the problem of discovering novel and interesting knowledge from large amount of data. This problem is often performed heuristically when the extraction of patterns is difficult using standard query mechanisms or classical statistical methods. In this paper a genetic programming framework, capable of performing an automatic discovery of classification rules easily comprehe...
متن کاملEvolving accurate and compact classification rules with gene expression programming
Classification is one of the fundamental tasks of data mining. Most rule induction and decision tree algorithms perform local, greedy search to generate classification rules that are often more complex than necessary. Evolutionary algorithms for pattern classification have recently received increased attention because they can perform global searches. In this paper, we propose a new approach fo...
متن کاملDiscovering Fuzzy Classification Rules with Genetic Programming and Co-evolution
In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evolutionary system for discovering fuzzy classification rules. The system uses two evolutionary algorithms: a genetic programming (GP) algorithm evolving a population of fuzzy rule sets and a simple evolutionary algorithm evolving a population of membership function definitions. The two populations co-...
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: Applied Artificial Intelligence
سال: 2005
ISSN: 0883-9514,1087-6545
DOI: 10.1080/08839510590967307