Acquiring Objective Functions in Distributed Rule-Based Systems from Examples

1996 ◽  
Vol 8 (5) ◽  
pp. 454-458
Author(s):  
Kenichi Matsuura ◽  
◽  
Yukinori Kakazu
Keyword(s):  
Problem Solving ◽  
Objective Function ◽  
Traveling Salesman ◽  
Rule Generation ◽  
Objective Functions ◽  
Rule Based ◽  
Rule Based System ◽  
System A ◽  
Distributed Problem Solving ◽  
Rule Based Systems

There are some great features in distributed problem solving systems, such as fault tolerance, robustness and so on. This system performs problem solving with search depending on an objective function. Distributed rulebased problem systems are considered to be of the same type. That is to say, the set of rules and the objective function exist separately within the system. However, in distributed rule-based systems, a set of rules should hold the objective function. The system should have a set of rules only, and the objective function should exist within that set of rules. In this paper, our objective is to acquire the objective function of a distributed rule-based system. A rule generation mechanism analyzes some given examples and acquires strategies for problem solving to a set of rules. In this way, the set of rules of the examples class in the domain represents the objective function of that class in the domain. Therefore, a solution using those rules keeps the same features as the examples if the problem belongs to the examples class that generates the set of rules. The system implemented by this theory has been applied to the domain of traveling salesman problem. This system has generated a set of rules that has held the objective function of its domain.

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.

scholarly journals Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

2020 ◽  
Vol 8 (5) ◽  
pp. 1335-1340
Keyword(s):  
Fuzzy Rule ◽  
Vital Role ◽  
Rule Based ◽  
Rule Based System ◽  
Proposed Model ◽  
Human Decision ◽  
Fuzzy Partitions ◽  
Accuracy Assessments ◽  
Improved Accuracy ◽  
Rule Based Systems

Fuzzy Rule Based Systems are playing vital role in the implementation of human decision making. The development of interpretable Fuzzy Rule Based Systems with improved accuracy is a crucial research aspect in fuzzy based systems. Mamdani type fuzzy rule based systems are used to implement the proposed model. In this manuscript a FRBS is implemented with Guaje Open-Access Java based software. The interpretability and accuracy assessments are recorded on the different experiments with various rule generation methods, like Fuzzy decision tree and Wang Mendel method. The results are found satisfactory and a trade-off is handled between interpretability and accuracy. The major concern of the experimentation is number and type of fuzzy partitions. K-means and Hierarchical Fuzzy Partitions are used in the experiments with three and five number of fuzzy partitions.

USING RULE STRUCTURE TO EVALUATE THE COMPLETENESS OF RULE-BASED SYSTEM TESTING: A CASE STUDY

2010 ◽  
Vol 20 (07) ◽  
pp. 975-986 ◽  
Author(s):  
STEPHEN C. MEDDERS ◽  
EDWARD B. ALLEN ◽  
EDWARD A. LUKE
Keyword(s):  
Test Suite ◽  
Application Framework ◽  
Test Coverage ◽  
Prior Work ◽  
Rule Based ◽  
Coverage Metrics ◽  
Rule Based System ◽  
Regression Test ◽  
Rule Based Systems

Rule-based systems are typically tested using a set of inputs which will produce known outputs. However, one does not know how thoroughly the software has been exercised. Traditional test-coverage metrics do not account for the dynamic data-driven flow of control in rule-based systems. Our literature review found that there has been little prior work on coverage metrics for rule-based systems. This paper proposes test-coverage metrics for rule-based systems derived from metrics defined by prior work, and presents an industrial scale case study. We conducted a case study to evaluate the practicality and usefulness of the proposed metrics. The case study applied the metrics to a system for computational fluid-dynamics models based on a rule-based application framework. These models were tested using a regression-test suite. The data-flow structure built by the application framework, along with the regression-test suite, provided case-study data. The test suite was evaluated against three kinds of coverage. The measurements indicated that complete coverage was not achieved, even at the lowest level definition. Lists of rules not covered provided insight into how to improve the test suite. The case study illustrated that structural coverage measures can be utilized to measure the completeness of rule-based system testing.

A LOGICAL ANALYSIS OF RULE INCONSISTENCY

2011 ◽  
Vol 05 (03) ◽  
pp. 271-280 ◽  
Author(s):  
PHILIPPE BESNARD
Keyword(s):  
Natural Deduction ◽  
Logical Analysis ◽  
Production Rules ◽  
Rule Based ◽  
Material Implication ◽  
Classical Sense ◽  
Rule Based System ◽  
New Information ◽  
Rule Consistency ◽  
Rule Based Systems

Representing knowledge in a rule-based system takes place by means of "if…then…" statements. These are called production rules for the reason that new information is produced when the rule fires. The logic attached to rule-based systems is taken to be classical inasmuch as "if…then…" is encoded by material implication. However, it appears that the notion of triggering "if…then…" amounts to different logical definitions. The paper investigates the matter, with an emphasis upon consistency because reading "if… then…" statements as rules calls for a notion of rule consistency that does not conform with consistency in the classical sense. Natural deduction is used to explore entailment and equivalence among various formulations and properties.

scholarly journals A Hybrid Approach to Sentiment Sentence Classification in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8961 ◽  
Author(s):  
Sunghwan Sohn ◽  
Manabu Torii ◽  
Dingcheng Li ◽  
Kavishwar Wagholikar ◽  
Stephen Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Hybrid Approach ◽  
Sentiment Classification ◽  
Learning System ◽  
Rule Based ◽  
Rule Based System ◽  
System A ◽  
Suicide Notes ◽  
Matching Rules

This paper describes the sentiment classification system developed by the Mayo Clinic team for the 2011 I2B2/VA/Cincinnati Natural Language Processing (NLP) Challenge. The sentiment classification task is to assign any pertinent emotion to each sentence in suicide notes. We have implemented three systems that have been trained on suicide notes provided by the I2B2 challenge organizer–-a machine learning system, a rule-based system, and a system consisting of a combination of both. Our machine learning system was trained on re-annotated data in which apparently inconsistent emotion assignment was adjusted. Then, the machine learning methods by RIPPER and multinomial Naïve Bayes classifiers, manual pattern matching rules, and the combination of the two systems were tested to determine the emotions within sentences. The combination of the machine learning and rule-based system performed best and produced a micro-average F-score of 0.5640.

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