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Correction to: Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery
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Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling
To increase the interpretability and prediction accuracy of the Machine Learning (ML) models, visualization of ML models is a key part of the ML process. Decision Trees (DTs) are essential in machine learning ...
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Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization
Building accurate and explainable/interpretable Machine Learning (ML) models for heterogeneous/mixed data is a long-standing challenge for algorithms designed for numeric data. This work focuses on develo** ...
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Visual Knowledge Discovery with General Line Coordinates
Understanding black-box Machine Learning methods on multidimensional data is a key challenge in Machine Learning. While many powerful Machine Learning methods already exist, these methods are often unexplainab...
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Full High-Dimensional Intelligible Learning in 2-D Lossless Visualization Space
This study explores a new methodology for machine learning classification tasks in 2-dimensional visualization space (2-D ML) using Visual knowledge Discovery in lossless General Line Coordinates. It is shown ...
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Parallel Coordinates for Discovery of Interpretable Machine Learning Models
This work uses visual knowledge discovery in parallel coordinates to advance methods of interpretable machine learning. The graphic data representation in parallel coordinates made the concepts of hypercubes a...
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Visual Explainable Machine Learning for High-Stakes Decision-Making with Worst Case Estimates
A major motivation for explaining and rigorous evaluating Machine Learning (ML) models is coming from high-stake decision-making tasks like cancer diagnostics, self-driving cars, and others with possible catas...
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Explainable Machine Learning and Visual Knowledge Discovery
The importance of visual methods in machine learning (ML) as tools to increase the interpretability and validity of models, is growing. The visual exploration of multidimensional data for knowledge discovery o...
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Interpretable Machine Learning forFinancial Applications
This chapter describes machine learning (ML) for financial applications with a focus on interpretable relational methods. It presents financial tasks, methodologies, and techniques in this ML area. It includes...
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Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions
Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/...
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Deep Learning Image Recognition for Non-images
Powerful deep learning algorithms open an opportunity for solving non-image Machine Learning (ML) problems by transforming these problems into the image recognition problems. The CPC-R algorithm presented in t...
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Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates
It is challenging for humans to enable visual knowledge discovery in data with more than 2–3 dimensions with a naked eye. This chapter explores the efficiency of discovering predictive machine learning models ...
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Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning
Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. Such algorithms fail to explain the model in terms of the domain they are designed for....
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Survey of Explainable Machine Learning with Visual and Granular Methods Beyond Quasi-Explanations
This chapter surveys and analyses visual methods of approaches with focus on moving from quasi-explanations that dominate in ML to actual domain-specific explanation supported by granular visuals. The impo...
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Enhancement of Cross Validation Using Hybrid Visual and Analytical Means with Shannon Function
The algorithm of k-fold cross validation is actively used to evaluate and compare machine learning algorithms. However, it has several important deficiencies documented in the literature along with its advantages...
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Enhancing Evaluation of Machine Learning Algorithms with Visual Means
Previous chapters demonstrated the ways of visual discovery of patterns using different General Line Coordinates. This chapter demonstrates the hybrid visual and analytical way to enhance the estimation of accura...
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Chapter
Pareto Front and General Line Coordinates
The Pareto Front is a mathematically correct solution of multi-objective optimization problems with several conflicting objectives. However, it is only a partial solution for many real-world situations, ...
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Chapter
Comparison and Fusion of Methods and Future Research
In this chapter, we first compare General Line Coordinates with other visualization methods that were not analyzed in the previous chapters yet. Then we summarize some comparisons that were presented in other...
