Classification rule learning using subgroup discovery of cross-domain attributes responsible for design-silicon mismatch

2010 ◽  
Author(s):  
Nicholas Callegari ◽  
Dragoljub (Gagi) Drmanac ◽  
Li-C. Wang ◽  
Magdy S. Abadir
Keyword(s):  
Rule Learning ◽  
Classification Rule ◽  
Subgroup Discovery ◽  
Cross Domain

scholarly journals SUBGROUP DISCOVERY OF THE MODY GENES;

2013 ◽  
Vol 20 (05) ◽  
pp. 644-652
Author(s):  
ATTIYA KANWAL ◽  
SAHAR FAZAL ◽  
SOHAIL ASGHAR ◽  
Muhammad Naeem
Keyword(s):  
Metabolic Disorders ◽  
Learning Algorithm ◽  
Rule Learning ◽  
Classification Rule ◽  
Subgroup Discovery ◽  
Autosomal Dominant Disorder ◽  
Data Set ◽  
Support Set ◽  
Confidence Threshold ◽  
Rule Sets

Background: The pandemic of metabolic disorders is accelerating in the urbanized world posing huge burden to healthand economy. The key pioneer to most of the metabolic disorders is Diabetes Mellitus. A newly discovered form of diabetes is MaturityOnset Diabetes of the Young (MODY). MODY is a monogenic form of diabetes. It is inherited as autosomal dominant disorder. Till to date11 different MODY genes have been reported. Objective: This study aims to discover subgroups from the biological text documentsrelated to these genes in public domain database. Data Source: The data set was obtained from PubMed. Period: September-December,2011. Materials and Methodology: APRIORI-SD subgroup discovery algorithm is used for the task of discovering subgroups. A wellknown association rule learning algorithm APRIORI is first modified into classification rule learning algorithm APRIORI-C. APRIORI-Calgorithm generates the rule from the discretized dataset with the minimum support set to 0.42% with no confidence threshold. Total 580rules are generated at the given support. APRIOIR-C is further modified by making adaptation into APRIORI-SD. Results: Experimentalresults demonstrate that APRIORI discovers the substantially smaller rule sets; each rule has higher support and significance. The rulesthat are obtained by APRIORI-C are ordered by weighted relative accuracy. Conclusion: Only first 66 rules are ordered as they cover therelation between all the 11 MODY genes with each other. These 66 rules are further organized into 11 different subgroups. The evaluationof obtained results from literature shows that APRIORI-SD is a competitive subgroup discovery algorithm. All the association amonggenes proved to be true.

Refinement and selection heuristics in subgroup discovery and classification rule learning

2017 ◽  
Vol 81 ◽  
pp. 147-162 ◽  
Author(s):  
Anita Valmarska ◽  
Nada Lavrač ◽  
Johannes Fürnkranz ◽  
Marko Robnik-Šikonja
Keyword(s):  
Rule Learning ◽  
Classification Rule ◽  
Subgroup Discovery

CoABCMiner: An Algorithm for Cooperative Rule Classification System Based on Artificial Bee Colony

2016 ◽  
Vol 25 (01) ◽  
pp. 1550028 ◽  
Author(s):  
Mete Celik ◽  
Fehim Koylu ◽  
Dervis Karaboga
Keyword(s):  
Artificial Bee Colony ◽  
Statistical Tests ◽  
Rule Learning ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Classification Rule ◽  
Data Sets ◽  
Classification Rules ◽  
Data Set ◽  
Bee Colony

In data mining, classification rule learning extracts the knowledge in the representation of IF_THEN rule which is comprehensive and readable. It is a challenging problem due to the complexity of data sets. Various meta-heuristic machine learning algorithms are proposed for rule learning. Cooperative rule learning is the discovery process of all classification rules with a single run concurrently. In this paper, a novel cooperative rule learning algorithm, called CoABCMiner, based on Artificial Bee Colony is introduced. The proposed algorithm handles the training data set and discovers the classification model containing the rule list. Token competition, new updating strategy used in onlooker and employed phases, and new scout bee mechanism are proposed in CoABCMiner to achieve cooperative learning of different rules belonging to different classes. We compared the results of CoABCMiner with several state-of-the-art algorithms using 14 benchmark data sets. Non parametric statistical tests, such as Friedman test, post hoc test, and contrast estimation based on medians are performed. Nonparametric tests determine the similarity of control algorithm among other algorithms on multiple problems. Sensitivity analysis of CoABCMiner is conducted. It is concluded that CoABCMiner can be used to discover classification rules for the data sets used in experiments, efficiently.

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