A Sensitivity-Based Improving Learning Algorithm for Madaline Rule II
نویسندگان
چکیده
منابع مشابه
A Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction
Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However ...
متن کاملA Cost-Sensitive Learning Algorithm for Fuzzy Rule-Based Classifiers
Designing classifiers may follow different goals. Which goal to prefer among others depends on the given cost situation and the class distribution. For example, a classifier designed for best accuracy in terms of misclassifications may fail when the cost of misclassification of one class is much higher than that of the other. This paper presents a decision-theoretic extension to make fuzzy rule...
متن کاملA Learning Algorithm for Multiple Rule Trees
It is one of the key problems for web based decision support systems to generate knowledge from huge database containing inconsistent information. In this paper, a learning algorithm for multiple rule trees (MRT) is developed, which is based on ID3 algorithm and rough set theory. MRT algorithm can quickly generate decision rules from inconsistent decision information tables. Both space and time...
متن کاملROCCER: An Algorithm for Rule Learning Based on ROC Analysis
We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules – for instance found by Apriori – using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose th...
متن کاملA Margin-based Model with a Fast Local Searchnewline for Rule Weighting and Reduction in Fuzzynewline Rule-based Classification Systems
Fuzzy Rule-Based Classification Systems (FRBCS) are highly investigated by researchers due to their noise-stability and interpretability. Unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. Rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. Most of the pro...
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: Mathematical Problems in Engineering
سال: 2014
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2014/219679