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.

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.

A Self-Tuning Fuzzy Rule-Based Classifier for Data Streams

2014 ◽  
Vol 22 (02) ◽  
pp. 293-303 ◽  
Author(s):  
Homeira Shahparast ◽  
Sam Hamzeloo ◽  
Mansoor Zolghadri Jahromi
Keyword(s):  
Data Streams ◽  
High Performance ◽  
Fuzzy Rule ◽  
Classification Problems ◽  
Online Data ◽  
Rule Based ◽  
Self Tuning ◽  
Rule Based Classifier ◽  
Concept Change ◽  
Expensive Process

In recent years, tremendous amounts of data streams are generated in different application areas. The new challenges in these data need fast and online data processing, especially in classification problems. One of the most challenging problems in field of data streams that reduces the performance of traditional methods is concept change. To handle this problem, it is necessary to update the classifier system after every alteration of the concept of data. However, updating a classifier can often be a time consuming and expensive process. In this paper, an efficient method is proposed for quickly and easily updating of a fuzzy rule-based classifier by setting a weight for each rule. Then, two online procedures for online adjustment of the rule weights are proposed. The experimental results show the high performance of these methods against a non-weighted approach.

Voting in fuzzy rule-based systems for pattern classification problems

1999 ◽  
Vol 103 (2) ◽  
pp. 223-238 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Tomoharu Nakashima ◽  
Takehiko Morisawa
Keyword(s):  
Pattern Classification ◽  
Fuzzy Rule ◽  
Classification Problems ◽  
Rule Based ◽  
Rule Based Systems

A Genetic Fuzzy-Rule-Based Classifier for Land Cover Classification From Hyperspectral Imagery

2012 ◽  
Vol 50 (1) ◽  
pp. 130-148 ◽  
Author(s):  
Dimitris G. Stavrakoudis ◽  
Georgia N. Galidaki ◽  
Ioannis Z. Gitas ◽  
John B. Theocharis
Keyword(s):  
Land Cover ◽  
Hyperspectral Imagery ◽  
Fuzzy Rule ◽  
Land Cover Classification ◽  
Rule Based ◽  
Rule Based Classifier

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.

Network anomaly detection using a fuzzy rule-based classifier

2017 ◽  
pp. 61-65
Author(s):  
Soumadip Ghosh ◽  
Arindrajit Pal ◽  
Amitava Nag ◽  
Shayak Sadhu ◽  
Ramsekher Pati
Keyword(s):  
Anomaly Detection ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Rule Based Classifier ◽  
Network Anomaly Detection
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