Note: [] Address for correspondence: Shimane University, 89-1 Enya-cho
Izumo Shimane, 6938501, JAPAN
Abstract: In datamining post-processing, rule selection with objective rule
evaluation indices is one of useful methods for extracting valuable knowledge
from mined patterns. However, the relationship between an index value and
experts' criteria has never been clarified. In order to determine
the relationship, we have developed a method to obtain learning models from a
dataset consisting of objective rule evaluation indices and evaluation labels
for rules. In this study, we have compared accuracies of classification
learning algorithms for datasets with randomized class labels. Then, the result
shows that accuracies of classification learning algorithms without any
criterion of a human expert can not outperform each percentage of majority
class on both of the balanced and imbalanced class distribution datasets. With
regarding to this result, we can determine whether or not a labeled rule set
contains some criteria based on the dataset consisting the objective rule
evaluation indices.
Keywords: Data Mining, Post-processing, Rule Evaluation, Learning Model
