Comparative Performance of Rule Quality Measures

نویسنده

  • A. Famili
چکیده

Table 2: Example of Contingency Table where = the number of examples covered by Rule R that are in Class C. = the number of examples covered by Rule R that are not in Class C. = the number of examples not covered by Rule R that are in Class C. = the number of examples covered by neither Rule R or Class C. = total number of examples covered by rule R. = total number of examples not covered rule R. = total number of examples in class C. = total number of examples not in class C. = total number of examples. R doesn't cover rc rc r rc rc r c c K rc rc rc rc r r c c K Comparative Performance of Rule Quality Measures ..

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Numeric Multi-Objective Rule Mining Using Simulated Annealing Algorithm

Abstract as a single objective one. Measures like support, confidence and other interestingness criteria which are used for evaluating a rule, can be thought of as different objectives of association rule mining problem. Support count is the number of records, which satisfies all the conditions that exist in the rule. This objective represents the accuracy of the rules extracted from the da...

متن کامل

An empirical comparison among quality measures for pattern based classifiers

Measuring the quality of a contrast pattern is an active and relevant area of pattern recognition and data mining. Quality measures are important tools in very different scenarios like supervised classification, pattern based clustering, and association rule mining. Consequently, and due to the large collection of available measures, it is important to perform comparative studies for each parti...

متن کامل

A Study of the Influence of Rule Measures in Classifiers Induced by Evolutionary Algorithms

The Pittsburgh representation is a well-known encoding for symbolic classifiers in evolutionary algorithms, where each individual represents one symbolic classifier, and each symbolic classifier is composed by a rule set. These rule sets can be interpreted as ordered or unordered sets. The major difference between these two approaches is whether rule ordering defines a rule precedence relations...

متن کامل

A Case Study for Learning from Imbalanced Data Sets

We present our experience in applying a rule induction technique to an extremely imbalanced pharmaceutical data set. We focus on using a variety of performance measures to evaluate a number of rule quality measures. We also investigate whether simply changing the distribution skew in the training data can improve predictive performance. Finally, we propose a method for adjusting the learning al...

متن کامل

Reliability Measures Measurement under Rule-Based Fuzzy Logic Technique

In reliability theory, the reliability measures contend the very important and depreciative role for any system analysis. Measurement of reliability measures is not easy due to ambiguity and vagueness which exist within reliability parameters. It is also very difficult to incorporate a large amount of uncertainty in well-established methodologies and techniques. However, fuzzy logic provides an...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1999