31 citations · 43 across the 10 of their papers we have counts for
10 papers
Estimating regression errors without ground truth values
Henri Tiittanen, Emilia Oikarinen, Andreas Henelius +1
Regression analysis is a standard supervised machine learning method used to model an outcome variable in terms of a set of predictor variables. In most real-world applications we…
Guided Visual Exploration of Relations in Data Sets
Kai Puolamäki, Emilia Oikarinen, Andreas Henelius
Efficient explorative data analysis systems must take into account both what a user knows and wants to know. This paper proposes a principled framework for interactive visual explo…
Randomisation Algorithms for Large Sparse Matrices
Kai Puolamäki, Andreas Henelius, Antti Ukkonen
In many domains it is necessary to generate surrogate networks, e.g., for hypothesis testing of different properties of a network. Furthermore, generating surrogate networks typica…
Human-guided data exploration using randomisation
Kai Puolamäki, Emilia Oikarinen, Buse Atli +1
An explorative data analysis system should be aware of what the user already knows and what the user wants to know of the data: otherwise the system cannot provide the user with th…
Human-Guided Data Exploration
Andreas Henelius, Emilia Oikarinen, Kai Puolamäki
The outcome of the explorative data analysis (EDA) phase is vital for successful data analysis. EDA is more effective when the user interacts with the system used to carry out the…
Interpreting Classifiers through Attribute Interactions in Datasets
Andreas Henelius, Kai Puolamäki, Antti Ukkonen
In this work we present the novel ASTRID method for investigating which attribute interactions classifiers exploit when making predictions. Attribute interactions in classification…