Posts in Category: computer science

computer science and informatics, information retrieval

riskyr: A toolbox for rendering risk literacy more transparent

Solving a problem simply means representing it
so as to make the solution transparent.

Simon, H.A. (1996). The Sciences of the Artificial

Hansjörg Neth, Felix Gaisbauer, Nico Gradwohl, Wolfgang Gaissmaier

riskyr: A toolbox for rendering risk literacy more transparent

Abstract:  Risk-related information — like the prevalence of conditions and the sensitivity and specificity of diagnostic tests or treatment decisions — can be expressed in terms of probabilities or frequencies. By providing a toolbox of methods and metrics, the R package riskyr computes, translates, and displays risk-related information in a variety of ways. Offering multiple complementary perspectives on the interplay between key parameters renders teaching and training of risk literacy more transparent.

Paper: FFTrees: An R toolbox to create, visualize, and evaluate FFTs

If a decision tree that measures up very well on the performance criterion
is nevertheless totally incomprehensible to a human expert, can it
be described as knowledge? Under the common-sense definition of this term
as material that might be assimilated and used by human beings, it is not…

J. Ross Quinlan (1987), p. 498

 

 

 

 

Nathaniel Phillips, Hansjörg Neth, Jan Woike, Wolfgang Gaissmaier

FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees

An example of an FFT (created by FFTrees) predicting heard disease.

An example of an FFT predicting the risk of having heart disease.

Abstract:  Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees.

Paper: Ranking LOD data with a cognitive heuristic

Arjon Buikstra, Hansjörg Neth, Lael J. Schooler, Annette ten Teije, Frank van Harmelen

Ranking query results from Linked Open Data using a simple cognitive heuristic

Abstract:  We address the problem how to select the correct answers to a query from among the partially incorrect answer sets that result from querying the Web of Data.