Extracting and reusing blocks of knowledge in learning classifier systems for text classification: a lifelong machine learning approach

2019 ◽  
Vol 23 (23) ◽  
pp. 12673-12682
Author(s):  
Muhammad Hassan Arif ◽  
Muhammad Iqbal ◽  
Jianxin Li
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Learning Classifier Systems ◽  
Learning Approach ◽  
Classifier Systems ◽  
Learning Classifier ◽  
Machine Learning Approach

USING ACCURACY-BASED LEARNING CLASSIFIER SYSTEMS FOR ADAPTABLE STRATEGY GENERATION IN GAMES AND INTERACTIVE VIRTUAL SIMULATIONS

2009 ◽  
Vol 10 (04) ◽  
pp. 365-390
Author(s):  
ALEJANDRO JUAN ◽  
RICHARD W. PAZZI ◽  
AZZEDINE BOUKERCHE
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Training Session ◽  
Learning Classifier Systems ◽  
Machine Learning Techniques ◽  
Knowledge Map ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Learning Classifier ◽  
Virtual Simulations

Historically, the artificial intelligence (AI) of interactive virtual simulations or games is usually driven by pre-defined static scripts. One of the disadvantages of such scripted opponents is that they can be deciphered and countered by an intelligent user. Thus, the user has the opportunity to find weaknesses and an easy solution against the virtual simulation, which diminishes the efficiency aspect of a training session or entertaining value drastically. While randomization can be used to mask the static behaviour of a scripted AI, it is possible to develop much richer solutions by applying Learning Classifier System (LCS) techniques to create agents with intelligent-like behaviors. Learning Classifier Systems are rule-based machine learning techniques that rely on a Genetic Algorithm to discover a knowledge map used to classify an input space into a set of actions. In this paper, we propose the use of an unsupervised machine learning technique called Accuracy-based Learning Classifier Systems (XCS) for adaptable strategy generation that can be used in virtual simulations or games. XCS use a Genetic Algorithm to evolve a knowledge base in the form of rules. The performance and adaptability of the strategies and tactics developed with the XCS is analyzed by facing these against scripted opponents on a real time strategy game. According to our experiments, the rulesets are able to adapt to a wide array of behaviors from its opponents, as opposed to a linear game script, which is limited in its ability to adapt to its environment.

scholarly journals Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

2009 ◽  
Vol 2009 ◽  
pp. 1-25 ◽  
Author(s):  
Ryan J. Urbanowicz ◽  
Jason H. Moore
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Discovery ◽  
Evolutionary Biology ◽  
Historical Review ◽  
Machine Learning Algorithms ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Rule Based ◽  
Learning Classifier

If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.

A Two-Stage Machine learning approach for temporally-robust text classification

2017 ◽  
Vol 69 ◽  
pp. 40-58 ◽  
Author(s):  
Thiago Salles ◽  
Leonardo Rocha ◽  
Fernando Mourão ◽  
Marcos Gonçalves ◽  
Felipe Viegas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Learning Approach ◽  
Two Stage ◽  
Machine Learning Approach

scholarly journals Comparative Study of Machine Learning Approach on Malay Translated Hadith Text Classification based on Sanad

2017 ◽  
Vol 135 ◽  
pp. 00066 ◽  
Author(s):  
Syuhairah Rahifah Mohammad Najib ◽  
Nurazzah Abd Rahman ◽  
Normaly Kamal Ismail ◽  
Nursyahidah Alias ◽  
Zulhilmi Mohamed Nor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Text Classification ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine Learning approach for Content Mining System

2021 ◽  
Vol 23 (06) ◽  
pp. 1569-1576
Author(s):  
Dr.A. Mekala ◽  
◽  
Dr.A. Prakash ◽  
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Text Classification ◽  
Text Categorization ◽  
Learning Approach ◽  
Mining System ◽  
Text Documents ◽  
Categorization Task ◽  
Machine Learning Approach ◽  
Content Mining

Text Classification (TC), also known as Text Categorization, is the mission of robotically classifying a set of text documents into dissimilar categories from a predefined set. If a manuscript belongs to exactly one of the categories, it is a single-label categorization task; otherwise, it is a multi-label categorization task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much consideration in the last years from both researchers in academia and manufacturing developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most appropriate documents.

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