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Found 15 papers, 7 papers with code
Using Slisemap to interpret physical data
Explainable artificial intelligence is used to investigate the decision processes of black box machine learning models and complex simulators.
SLISEMAP: Supervised dimensionality reduction through local explanations
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item.
Interactive Causal Structure Discovery in Earth System Sciences
We present a workflow that is required to take this knowledge into account and to apply CSD algorithms in Earth system sciences.
Tell Me Something I Don't Know: Randomization Strategies for Iterative Data Mining
At each step in the data mining process, the randomization produces random samples from the set of data matrices satisfying the already discovered patterns or models.
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field.
Estimating regression errors without ground truth values
Regression analysis is a standard supervised machine learning method used to model an outcome variable in terms of a set of predictor variables.
Guided Visual Exploration of Relations in Data Sets
This paper proposes a principled framework for interactive visual exploration of relations in data, through views most informative given the user's current knowledge and objectives.
Human-guided data exploration using randomisation
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 the most informative and useful views of the data.
Human-Guided Data Exploration
This framework allows the user to incorporate existing knowledge into the exploration process, focus on exploring a subset of the data, and compare different complex hypotheses concerning relations in the data.
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach
We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.
Subjectively Interesting Subgroup Discovery on Real-valued Targets
The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.
Interpreting Classifiers through Attribute Interactions in Datasets
In this work we present the novel ASTRID method for investigating which attribute interactions classifiers exploit when making predictions.
Multivariate Confidence Intervals
In this paper we define confidence intervals for multivariate data that extend the one-dimensional definition in a natural way.
Clustering with Confidence: Finding Clusters with Statistical Guarantees
In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core clusters, which correspond to clusters where the co-occurrence probability of each data item within a cluster is at least $1 - \alpha$.
Finding Statistically Significant Attribute Interactions
In many data exploration tasks it is meaningful to identify groups of attribute interactions that are specific to a variable of interest.
