scholarly journals Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics

2006 ◽  
pp. 18-29 ◽  
Author(s):  
Hendrik Blockeel ◽  
Leander Schietgat ◽  
Jan Struyf ◽  
Sašo Džeroski ◽  
Amanda Clare
Keyword(s):  
Functional Genomics ◽  
Decision Trees ◽  
Multilabel Classification ◽  
Hierarchical Multilabel Classification

Combining decision trees classifiers: a case study of automatic sleep stage scoring

2012 ◽  
Vol 2 (1) ◽  
pp. 60 ◽  
Author(s):  
Natheer Khasawneh ◽  
Stefan Conrad ◽  
Luay Fraiwan ◽  
Eyad Taqieddin ◽  
Basheer Khasawneh
Keyword(s):  
Decision Trees ◽  
Sleep Stage ◽  
Sleep Stage Scoring

scholarly journals Decision-making on Implementation of IPO Under Topological Uncertainty

2015 ◽  
Vol 63 (1) ◽  
pp. 193-200 ◽  
Author(s):  
Karel Doubravský ◽  
Tomáš Meluzín ◽  
Mirko Dohnal
Keyword(s):  
Decision Making ◽  
Decision Trees ◽  
Initial Public Offering ◽  
Decision Makers ◽  
Structure Topology ◽  
Complex Decision Making ◽  
Flexible Integration ◽  
Complex Decision ◽  
Public Offering

IPO (Initial Public Offering) is a complex decision making task which is always associated with different types of uncertainty. Poor accuracies of available probabilities of lotteries e.g. quantification of investor interest is studied in the first part of this paper (Meluzín, Doubravský, Dohnal, 2012). However, IPO is often prohibitively ill-known. This paper takes into consideration the fact that decision makers cannot specify the structure/topology of the relevant decision tree. It means that one IPO task is specified by several (partially) different decision trees which comes from different sources e.g. from different teams of decision makers/experts. A flexible integration of those trees is based on fuzzy logic using the reconciliation (Meluzín, Doubravský, Dohnal, 2012). The developed algorithm is demonstrated by a case study which is presented in details. The IPO case integrates two partially different decision trees.

Discussing Anomalous Situations using Decision Trees: A Head Injury Case Study

2001 ◽  
Vol 40 (05) ◽  
pp. 373-379 ◽  
Author(s):  
A. McQuatt ◽  
P. J. D. Andrews ◽  
V. Corruble ◽  
P. A. Jones ◽  
D. Sleeman
Keyword(s):  
Head Injury ◽  
Decision Trees ◽  
Demographic Data ◽  
Patient Data ◽  
Physiological Data ◽  
Royal Infirmary ◽  
Domain Experts ◽  
Seriously Ill Patients ◽  
Seriously Ill

Summary Objectives: Predicting the outcome of seriously ill patients is a challenging problem for clinicians. Methods: One alternative to clinical trials is to analyse existing patient data in an attempt to predict the several outcomes, and to suggest therapies. In this paper we use decision tree techniques to predict the outcome of head injury patients. The work is based on patient data from the Edinburgh Royal Infirmary which contains both background (demographic) data and temporal (physiological) data. Results: The focus of this paper is the discussion of the anomalous cases in the decision trees with the domain experts (the clinicians). Conclusions: These analyses led to the detection of several situations where both the data analysis and patient data collection should be enhanced, which in turn should lead to improved patient care.

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