scholarly journals Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

2015 ◽  
Author(s):  
Marcin Michalak ◽  
Marek Sikora ◽  
Łukasz Wróbel
Keyword(s):  
Quality Measures ◽  
Rule Induction ◽  
Empirical Approach ◽  
Sequential Covering

Study of population dynamics of soybean semi-looper Gesonia gemmaSwinhoe by using rule induction model in Maharashtra, India

2017 ◽  
Author(s):  
J. Cruz Antony ◽  
M. Pratheepa
Keyword(s):  
Population Dynamics ◽  
Control Strategy ◽  
Rule Induction ◽  
Maximum Temperature ◽  
Classification Rules ◽  
Target Feature ◽  
Sequential Covering ◽  
Heavy Damage ◽  
Induction Model ◽  
Rainy Days

Gesonia gemma Swinhoe (1885) is a grey semi-looper and it has emerged as a serious threat to the soybean crop. This defoliator causes heavy damage to the crop in the form of loss in grain weight. Gesonia gemma population dynamics was studied in various districts of Maharashtra. Sequential covering algorithm (CN2 rule induction) has been proposed for rule induction model to generate a list of classification rules with target feature (G. gemma population) and the independent abiotic features. The classification rules have exhibited more accuracy and showed that maximum temperature and humidity with less number of rainy days has influenced the population of Gesonia gemma in Maharashtra. Hence, this rule induction model can be used to study the collected evidence for prediction and it will be helpful to the farmers to take necessary pest control strategy.

Study of the polymorphism of the PatagonianCalceolaria polyrhiza(Calceolariaceae) using decision tree and sequential covering rule induction

2013 ◽  
Vol 173 (3) ◽  
pp. 487-500 ◽  
Author(s):  
Marina M. Strelin ◽  
Andrea Cosacov ◽  
Martin Diller ◽  
Alicia N. Sérsic
Keyword(s):  
Decision Tree ◽  
Rule Induction ◽  
Sequential Covering

Censoring Weighted Separate-and-Conquer Rule Induction from Survival Data

2014 ◽  
Vol 53 (02) ◽  
pp. 137-148 ◽  
Author(s):  
M. Sikora ◽  
Ł. Wróbel
Keyword(s):  
Survival Data ◽  
Predictive Accuracy ◽  
Rule Learning ◽  
Learning Rule ◽  
Quality Measures ◽  
Rule Induction ◽  
Weighting Scheme ◽  
Patient Treatment ◽  
Rule Based ◽  
Wide Range

SummaryObjectives: Rule induction is one of the major methods of machine learning. Rule-based models can be easily read and interpreted by humans, that makes them particularly useful in survival studies as they can help clinicians to better understand analysed data and make informed decisions about patient treatment. Although of such usefulness, there is still a little research on rule learning in survival analysis. In this paper we take a step towards rule-based analysis of survival data.Methods: We investigate so-called covering or separate-and-conquer method of rule induction in combination with a weighting scheme for handling censored observations. We also focus on rule quality measures being one of the key elements differentiating particular implementations of separate-and-conquer rule induction algorithms. We examine 15 rule quality measures guiding rule induction process and reflecting a wide range of different rule learning heuristics.Results: The algorithm is extensively tested on a collection of 20 real survival datasets and compared with the state-of-the-art survival trees and random survival forests algorithms. Most of the rule quality measures outperform Kaplan-Meier estimate and perform at least equally well as tree-based algorithms.Conclusions: Separate-and-conquer rule induction in combination with weighting scheme is an effective technique for building rule-based models of survival data which, according to predictive accuracy, are competitive with tree-based representations.

Sequential covering rule induction algorithm for variable consistency rough set approaches

2011 ◽  
Vol 181 (5) ◽  
pp. 987-1002 ◽  
Author(s):  
Jerzy Błaszczyński ◽  
Roman Słowiński ◽  
Marcin Szeląg
Keyword(s):  
Rough Set ◽  
Rule Induction ◽  
Sequential Covering

scholarly journals РОЗРОБКА КЛІНІЧНОЇ ДІАГНОСТИЧНОЇ СИСТЕМИ, ЩО ҐРУНТУЄТЬСЯ НА ПРАВИЛАХ, ПОБУДОВАНИХ МЕТОДОМ ПОСЛІДОВНОГО ПОКРИТТЯ

2015 ◽  
Author(s):  
O. O. Stakhanska
Keyword(s):  
Computational Complexity ◽  
Rule Induction ◽  
Training Data ◽  
Data Sets ◽  
Diagnostic Systems ◽  
Clinical Diagnostic ◽  
Sequential Covering

The work deals with the computational complexity of the rule induction algorithm based on sequential covering when developing clinical diagnostic systems. Established evaluation confirmed experimentally as a change in the amount of attributes, and the volume of training data sets.

scholarly journals ПРО КЛІНІЧНУ ЕКСПЕРТНУ СИСТЕМУ, ЩО ГРУНТУЄТЬСЯ НА ПРАВИЛАХ, НА ОСНОВІ ТЕХНОЛОГІ DATA MINING

2015 ◽  
Author(s):  
V. P. Martsenyuk ◽  
O. O. Stakhanska
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Rule Induction ◽  
Software Implementation ◽  
Induction Method ◽  
Sequential Covering

In the work the topics of software implementation of rule induction method based on sequential covering algorithm are considered. Such approach allows us to develop clinical decision support system. The project is implemented within Netbeans IDE based on Java-classes.

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