A FILTER PROPOSAL FOR INCLUDING FEATURE CONSTRUCTION IN A GENETIC LEARNING ALGORITHM

2012 ◽  
Vol 20 (supp02) ◽  
pp. 31-49 ◽  
Author(s):  
DAVID GARCIA ◽  
ANTONIO GONZALEZ ◽  
RAUL PEREZ
Keyword(s):  
Learning Algorithm ◽  
A Priori ◽  
Fuzzy Rule ◽  
Feature Construction ◽  
Rule Based ◽  
Filter Model ◽  
Information Measures ◽  
Construction Techniques ◽  
Basic Learning ◽  
Current Proposal

In system identification process often a predetermined set of features is used. However, in many cases it is difficult to know a priori whether the selected features were really the more appropriate ones. This is the reason why the feature construction techniques have been very interesting in many applications. Thus, the current proposal introduces the use of these techniques in order to improve the description of fuzzy rule-based systems. In particular, the idea is to include feature construction in a genetic learning algorithm. The construction of attributes in this study will be restricted to the inclusion of functions defined on the initial attributes of the system. Since the number of functions and the number of attributes can be very large, a filter model, based on the use of information measures, is introduced. In this way, the genetic algorithm only needs to explore the particular new features that may be of greater interest to the final identification of the system. In order to manage the knowledge provided by the new attributes based on the use of functions we propose a new model of rule by extending a basic learning fuzzy rule-based model. Finally, we show the experimental study associated with this work.

Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems

2007 ◽  
Vol 11 (6) ◽  
pp. 546-553 ◽  
Author(s):  
Tomoharu Nakashima ◽  
◽  
Yasuyuki Yokota ◽  
Hisao Ishibuchi ◽  
Gerald Schaefer ◽  
...  
Keyword(s):  
Pattern Classification ◽  
A Priori ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Classification Error ◽  
Rule Generation ◽  
Classification Problems ◽  
Misclassification Cost ◽  
Rule Based ◽  
Rule Based Systems

We evaluate the performance of cost-sensitive fuzzy-rule-based systems for pattern classification problems. We assume that a misclassification cost is given a priori for each training pattern. The task of classification thus becomes to minimize both classification error and misclassification cost. We examine the performance of two types of fuzzy classification based on fuzzy if-then rules generated from training patterns. The difference is whether or not they consider misclassification costs in rule generation. In our computational experiments, we use several specifications of misclassification cost to evaluate the performance of the two classifiers. Experimental results show that both classification error and misclassification cost are reduced by considering the misclassification cost in fuzzy rule generation.

Designing Self-Organizing Systems With Deep Multi-Agent Reinforcement Learning

2019 ◽  
Author(s):  
Hao Ji ◽  
Yan Jin
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Task Complexity ◽  
A Priori ◽  
Scale Up ◽  
Simple Task ◽  
Rule Based ◽  
Social Rule ◽  
Multi Agent ◽  
Self Organizing

Abstract Self-organizing systems (SOS) are able to perform complex tasks in unforeseen situations with adaptability. Previous work has introduced field-based approaches and rule-based social structuring for individual agents to not only comprehend the task situations but also take advantage of the social rule-based agent relations in order to accomplish their overall tasks without a centralized controller. Although the task fields and social rules can be predefined for relatively simple task situations, when the task complexity increases and task environment changes, having a priori knowledge about these fields and the rules may not be feasible. In this paper, we propose a multi-agent reinforcement learning based model as a design approach to solving the rule generation problem with complex SOS tasks. A deep multi-agent reinforcement learning algorithm was devised as a mechanism to train SOS agents for acquisition of the task field and social rule knowledge, and the scalability property of this learning approach was investigated with respect to the changing team sizes and environmental noises. Through a set of simulation studies on a box-pushing problem, the results have shown that the SOS design based on deep multi-agent reinforcement learning can be generalizable with different individual settings when the training starts with larger number of agents, but if a SOS is trained with smaller team sizes, the learned neural network does not scale up to larger teams. Design of SOS with a deep reinforcement learning model should keep this in mind and training should be carried out with larger team sizes.

scholarly journals ON DESIGNING FUZZY RULE-BASED MULTICLASSIFICATION SYSTEMS BY COMBINING FURIA WITH BAGGING AND FEATURE SELECTION

2011 ◽  
Vol 19 (04) ◽  
pp. 589-633 ◽  
Author(s):  
KRZYSZTOF TRAWIŃSKI ◽  
OSCAR CORDÓN ◽  
ARNAUD QUIRIN
Keyword(s):  
Feature Selection ◽  
Learning Algorithm ◽  
Rule Learning ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Classification Rule ◽  
High Dimensional ◽  
Global Approach ◽  
Rule Based ◽  
Exhaustive Study

In this work, we conduct a study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging and feature selection. We develop an exhaustive study on the potential of bagging and feature selection to design a final FURIA-based fuzzy multiclassifier dealing with high dimensional data. Several parameter settings for the global approach are tested when applied to twenty one popular UCI datasets. The results obtained show that FURIA-based fuzzy multiclassifiers outperform the single FURIA classifier and are competitive with C4.5 multiclassifiers and random forests.

Cost-Sensitive Classification for Medical Diagnosis

2008 ◽  
pp. 297-302 ◽  
Author(s):  
Gerald Schaefer ◽  
Tomoharu Nakashima ◽  
Yasuyuki Yokota
Keyword(s):  
Pattern Classification ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Breast Cancer Dataset ◽  
Classification Problems ◽  
Rule Based ◽  
Cost Sensitive Classification ◽  
Minimisation Problem ◽  
Rule Based Classifier

In this article, we present a cost-sensitive approach to medical diagnosis based on fuzzy rule-based classification (Schaefer, Nakashima, Yokota, & Ishibuchi, 2007). While fuzzy rule-based systems have been mainly employed for control problems (Lee, 1990) more recently they have also been applied to pattern classification problems (Ishibuchi & Nakashima, 1999; Nozaki, Ishibuchi, & Tanaka, 1996). We modify a fuzzy rule-based classifier to incorporate the concept of weight which can be considered as the cost of an input pattern being misclassified. The pattern classification problem is thus reformulated as a cost minimisation problem. Based on experimental results on the Wisconsin breast cancer dataset, we demonstrate the efficacy of our approach. We also show that the application of a learning algorithm can further improve the classification performance of our classifier.

Self-Learning Fuzzy Logic Controller using Q-Learning

2000 ◽  
Vol 4 (5) ◽  
pp. 349-354 ◽  
Author(s):  
Min-Soeng Kim ◽  
◽  
Sun-Gi Hong ◽  
Ju-Jang Lee
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Learning Algorithm ◽  
A Priori ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Q Learning ◽  
Continuous Output ◽  
Continuous Actions

Fuzzy logic controllers consist of if-then fuzzy rules generally adopted from a priori expert knowledge. However, it is not always easy or cheap to obtain expert knowledge. Q-learning can be used to acquire knowledge from experiences even without the model of the environment. The conventional Q-learning algorithm cannot deal with continuous states and continuous actions. However, the fuzzy logic controller can inherently receive continuous input values and generate continuous output values. Thus, in this paper, the Q-learning algorithm is incorporated into the fuzzy logic controller to compensate for each method’s disadvantages. Modified fuzzy rules are proposed in order to incorporate the Q-learning algorithm into the fuzzy logic controller. This combination results in the fuzzy logic controller that can learn through experience. Since Q-values in Q-learning are functional values of the state and the action, we cannot directly apply the conventional Q-learning algorithm to the proposed fuzzy logic controller. Interpolation is used in each modified fuzzy rule so that the Q-value is updatable.

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