Rule Quality Measures Settings in Classification, Regression and Survival Rule Induction — an Empirical Approach

2016 ◽  
Vol 149 (4) ◽  
pp. 419-449 ◽  
Author(s):  
Łukasz Wróbel ◽  
Marek Sikora ◽  
Marcin Michalak
Keyword(s):  
Quality Measures ◽  
Rule Induction ◽  
Empirical Approach

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.

The Elicited Language Analysis Procedure

1982 ◽  
Vol 13 (1) ◽  
pp. 37-41
Author(s):  
Larry J. Mattes
Keyword(s):  
Diagnostic Tool ◽  
Descriptive Analysis ◽  
Quality Measures ◽  
Analysis Procedure ◽  
Elicited Imitation ◽  
Response Quality ◽  
Semantic Relationships ◽  
Language Analysis

Elicited imitation tasks are frequently used as a diagnostic tool in evaluating children with communication handicaps. This article presents a scoring procedure that can be used to obtain an in-depth descriptive analysis of responses produced on elicited imitation tasks. The Elicited Language Analysis Procedure makes it possible to systematically evaluate responses in terms of both their syntactic and semantic relationships to the stimulus sentences presented by the examiner. Response quality measures are also included in the analysis procedure.

New Post-Acute Care Quality Measures Take Effect Oct. 1

ASHA Leader ◽  
2016 ◽  
Vol 21 (6) ◽  
pp. 34-35
Author(s):  
Sarah Warren ◽  
Tim Nanof
Keyword(s):  
Acute Care ◽  
Quality Measures ◽  
Care Quality ◽  
Post Acute Care

Audiology Consortium Develops Quality Measures

ASHA Leader ◽  
2009 ◽  
Vol 14 (8) ◽  
pp. 5-5
Author(s):  
Kate Romanow ◽  
Anne Oyler
Keyword(s):  
Quality Measures
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