Knowledge discovery about scientific papers or proceedings referenced NASA/DAAC data with a rule-based classifier

2002 ◽  
Author(s):  
Donglian Sun ◽  
Chris Lynnes ◽  
Richard K. Kiang ◽  
Steve Kempler ◽  
George Serafino
Keyword(s):  
Knowledge Discovery ◽  
Rule Based ◽  
Scientific Papers ◽  
Rule Based Classifier

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 semi-supervised deep rule-based classifier for robust finger knuckle-print verification

2022 ◽  
Author(s):  
Mounir Benmalek ◽  
Abdelouahab Attia ◽  
Abderraouf Bouziane ◽  
M. Hassaballah
Keyword(s):  
Rule Based ◽  
Rule Based Classifier

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

scholarly journals Improving rule-based classification using Harmony Search

2019 ◽  
Vol 5 ◽  
pp. e188 ◽  
Author(s):  
Hesam Hasanpour ◽  
Ramak Ghavamizadeh Meibodi ◽  
Keivan Navi
Keyword(s):  
Harmony Search ◽  
Search Space ◽  
Classification Model ◽  
Classification Algorithms ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Rule Based ◽  
Rule Based Classifier ◽  
High Processing ◽  
Fundamental Limitation

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS

2016 ◽  
Vol 83 (1) ◽  
pp. 97-127 ◽  
Author(s):  
Binh Thai Pham ◽  
Dieu Tien Bui ◽  
Indra Prakash ◽  
M. B. Dholakia
Keyword(s):  
Fuzzy Rule ◽  
Spatial Prediction ◽  
Classifier Ensemble ◽  
Rotation Forest ◽  
Rule Based ◽  
Rule Based Classifier
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