Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets

2007 ◽  
Author(s):  
Florian Verh ◽  
Sanjay Chawla
Keyword(s):  
Associative Classification ◽  
Imbalanced Datasets

scholarly journals A MapReduce solution for associative classification of big data

2016 ◽  
Vol 332 ◽  
pp. 33-55 ◽  
Author(s):  
Alessio Bechini ◽  
Francesco Marcelloni ◽  
Armando Segatori
Keyword(s):  
Big Data ◽  
Associative Classification

scholarly journals Associative classification of the Jordanian hospitals efficiency based on DEA

2018 ◽  
Vol 8 (3) ◽  
pp. 120-125
Author(s):  
Ahmad Alaiad ◽  
Hassan Najadat ◽  
Nusaiba Al-Mnayyis ◽  
Ashwaq Khalil
Keyword(s):  
Data Mining ◽  
Decision Makers ◽  
Healthcare Sector ◽  
Data Envelopment ◽  
Associative Classification ◽  
Dea Model ◽  
Knowledge Analysis ◽  
Minimum Number ◽  
And Performance

Data envelopment analysis (DEA) has been widely used in many fields. Recently, it has been adopted by the healthcare sector to improve efficiency and performance of the healthcare organisations, and thus, reducing overall costs and increasing productivity. In this paper, we demonstrate the results of applying the DEA model in Jordanian hospitals. The dataset consists of 28 hospitals and is classified into two groups: efficient and non-efficient hospitals. We applied different association classification data mining techniques (JCBA, WeightedClassifier and J48) to generate strong rules using the Waikato Environment for Knowledge Analysis. We also applied the open source DEA software and MaxDEA software to manipulate the DEA model. The results showed that JCBA has the highest accuracy. However, WeightedClassifier method achieves the highest number of generated rules, while the JCBA method has the minimum number of generated rules. The results have several implications for practice in the healthcare sector and decision makers. Keywords: Component, DEA, DMU, output-oriented model, health care system.

Behavior classification of goats using 9-axis multi sensors: The effect of imbalanced datasets on classification performance

2019 ◽  
Vol 166 ◽  
pp. 105027 ◽  
Author(s):  
Koki Sakai ◽  
Kazato Oishi ◽  
Masafumi Miwa ◽  
Hajime Kumagai ◽  
Hiroyuki Hirooka
Keyword(s):  
Classification Performance ◽  
Imbalanced Datasets

Classification of antimicrobial peptides with imbalanced datasets

2015 ◽  
Author(s):  
Francy L. Camacho ◽  
Rodrigo Torres ◽  
Raúl Ramos Pollán
Keyword(s):  
Antimicrobial Peptides ◽  
Imbalanced Datasets
Sign in / Sign up

Export Citation Format

Share Document