GaCoVi: a Correlation Visualization to Support Interpretability-Aware Feature Selection for Regression Models

Rojo, Diego; Htun, Nyi Nyi; Verbert, Katrien

doi:10.2312/evs.20201060

Cited by 3 publications

(4 citation statements)

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“…With FeatureEnVi, we improve the total combined score by using 6 well-engineered features instead of the original 11 features. On the contrary, Rojo et al [68], who treated this task as a regression problem, found that the performance decreased slightly when they selected 6 features.…”

Section: Process Tracker and Predictive Resultsmentioning

confidence: 97%

“…In FeatureEnVi, determining which features to match during feature generation is achieved by analyzing linear and nonlinear relations present in the data. For the former, one of the most well-known approaches is Pearson's Correlation Coefficient between features and with the target variable [68,85]. For the latter, mutual information is used in our VA system (also used by May et al [55], for instance).…”

Section: Feature Generationmentioning

confidence: 99%

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FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches

Chatzimparmpas,

Martins,

Kucher

et al. 2021

Preprint

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Fig. 1. Selecting important features, transforming them, and generating new features with FeatureEnVi: (a) the horizontal beeswarm plot for manually slicing the data space (which is sorted by predicted probabilities) and continuously checking the migration of data instances throughout the process; (b) the table heatmap view for the selection of features according to feature importances calculated from automatic techniques; (c) the radial tree providing an overview of the features with statistical measures for the different groups of instances, as set by the user-defined data slices; (d) the graph visualization for the detailed exploration of features, their transformation, and comparison between two or three features for feature generation purposes; and (e) the punchcard for tracking the steps of the process and the grouped bar chart for comparing the current vs. the best predictive performance based on three validation metrics.

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Section: Process Tracker and Predictive Resultsmentioning

confidence: 97%

Section: Feature Generationmentioning

confidence: 99%

FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches

Chatzimparmpas,

Martins,

Kucher

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…All in all, with FeatureEnVi, we improve the total combined score by using 6 well-engineered features instead of the original 11. On the contrary, Rojo et al [541] reported a slight decrease in performance when selecting 6 features for this task as a regression problem.…”

Section: Process Tracker and Predictive Resultsmentioning

confidence: 89%

“…For the former, one of the most well-known approaches is Pearson's correlation coefficient between features and with the target variable [541,741]. For the latter, mutual information is used in our VA system (also used by May et al [442], for instance).…”

Section: Feature Generationmentioning

confidence: 99%

Visual Analytics for Explainable and Trustworthy Machine Learning

Chatzimparmpas¹

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The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. While automated methods can partly handle these problems, recent research findings suggest that their combination with innovative methods developed within information visualization and visual analytics can lead to further insights gained from models and, consequently, improve their predictive ability and enhance trustworthiness in the entire process. Visual analytics is the area of research that studies the analysis of vast and intricate information spaces by combining statistical and machine learning models with interactive visual interfaces. By following this methodology, human experts can better understand such spaces and apply their domain expertise in the process of building and improving the underlying models. The primary goals of this dissertation are twofold, focusing on (1) methodological aspects, by conducting qualitative and quantitative meta-analyses to support the visualization research community in making sense of its literature and to highlight unsolved challenges, as well as (2) technical solutions, by developing visual analytics approaches for various machine learning models, such as dimensionality reduction and ensemble learning methods. Regarding the first goal, we define, categorize, and examine in depth the means for visual coverage of the different trust levels at each stage of a typical machine learning pipeline and establish a design space for novel visualizations in the area. Regarding the second goal, we discuss multiple visual analytics tools and systems implemented by us to facilitate the underlying research on the various stages of the machine learning pipeline, i.e., data processing, feature engineering, hyperparameter tuning, understanding, debugging, refining, and comparing models. Our approaches are data-agnostic, but mainly target tabular data with meaningful attributes in diverse domains, such as health care and finance. The applicability and effectiveness of this work were validated with case studies, usage scenarios, expert interviews, user studies, and critical discussions of limitations and alternative designs. The results of this dissertation provide new avenues for visual analytics research in explainable and trustworthy machine learning. Keywords: visualization, interaction, visual analytics, explainable machine learning, XAI, trustworthy machine learning, ensemble learning, dimensionality reduction, supervised learning, unsupervised learning, ML, AI, tabular data

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Developing Visual-Assisted Decision Support Systems across Diverse Agricultural Use Cases

et al. 2022

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Decision support systems (DSSs) in agriculture are becoming increasingly popular, and have begun adopting visualisations to facilitate insights into complex data. However, DSSs for agriculture are often designed as standalone applications, which limits their flexibility and portability. They also rarely provide interactivity, visualise uncertainty and are evaluated with end-users. To address these gaps, we developed six web-based visual-assisted DSSs for various agricultural use cases, including biological efficacy correlation analysis, water stress and irrigation requirement analysis, product price prediction, etc. We then evaluated our DSSs with domain experts, focusing on usability, workload, acceptance and trust. Results showed that our systems were easy to use and understand, and participants perceived them as highly performant, even though they required a slightly high mental demand, temporal demand and effort. We also published the source code of our proposed systems so that they can be re-used or adapted by the agricultural community.

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GaCoVi: a Correlation Visualization to Support Interpretability-Aware Feature Selection for Regression Models

Cited by 3 publications

References 0 publications

FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches

FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches

Visual Analytics for Explainable and Trustworthy Machine Learning

Developing Visual-Assisted Decision Support Systems across Diverse Agricultural Use Cases

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