Learn about Randomized Base Classifiers in Data Mining, their concepts, types, and applications for improving prediction accuracy.
Although the success of the rule-based classifiers using meta-heuristics such as Ant-Miner, BeeMiner etc. in data mining has been demonstrated, any implementation of these classification algorithms with differential privacy has not been proposed in the literature until now to our best knowledge. ...
Effectiveness evaluation of rule based classifiers for the classification of iris data set. Bonfring International Journal of Man Machine Interface, 1(5): 05-09.C Lakshmi Devasena, T Sumathi, VV Gomathi, and M Hemalatha. Effectiveness evalu- ation of rule based classifiers for the ...
In general, the optimality of feature subset can substantially improve the interpretability of rule-based classifiers since the optimal minimal number of features minimizes the number of classification rules generated from data. The FS techniques can be broadly classified into filter-based and wrapper-...
Experiments show that the RCBT classifier can match or outperform other state-of-the-art classifiers on several benchmark gene expression datasets. In addition, the top-k covering rule groups themselves provide insights into the mechanisms responsible for diseases directly. 展开 ...
A variant of performing such a method based on the application of fuzzy classifier is offered in the work. The author’s algorithm of the formation of bases of fuzzy rules which feature consists in application of mining clustering after carrying out of adjustment of parameters on concrete data ...
but combine the binary classifiers trained on each possible label to a multiclass classifier. In order to do that, we construct a binary vector of length equal to the number of labels. Each entry represents the prediction of a rule set learned in advance to classify whether the corresponding ...
Reference may again be had to the publication “Mining concept-drifting data streams using ensemble classifiers” Wang, H. SIGKDD, ACM Press, San Francisco, Calif., 2003. In order to use ensemble classifies to make a prediction, the model used must evaluate all of the classifiers in the ...
Classification rule learning: It is a form of supervised learning where the algorithm learns from labeled data to generate rules automatically. In the context of cybersecurity, Decision trees [66], random forests [67], and other rule-based classifiers [5] can be used to categorize data instance...
It introduces a customized distance measure technique to handle parallel units of fuzzy rule-based classifiers, representing discrete medical conditions, for computing and quantifying the similarity between feature vectors of electronic health records in a discrete multivariate data feature space. 2. Fuzzy...