Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning a…
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48 results for “Visual Analytics”
The abstract discusses how humans use visualizations in machine learning.
problem The reliance on human involvement in AI systems and analytics.
method Review of seven steps in the ML process and different visualization techniques.
result Different visualizations are used at various stages of the ML process.
Developing a visual platform for faster astronomical source cataloging.
problem Speeding up cataloging of large area surveys in radio astronomy.
method Integration of advanced source finding and classification tools into a visual analytic platform.
result Improvement and acceleration of cataloging process in astronomical surveys.
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoni…
Visual system compares and evaluates machine learning models for clinical data predictions.
problem Challenges in comparing and evaluating different machine learning models for medical predictions.
method Developed a visual analytics system to compare and evaluate multiple models' prediction criteria and consistency.
result Demonstrated the effectiveness of the visual analytics system in assisting clinicians and researchers.
regvis.net offers a visual survey of regulatory visualization.
problem Lack of a comprehensive resource for regulatory visualization.
method Collection and manual tagging of 80+ publications, creation of a searchable webpage.
result First publication set tailored for regulatory visualization.
Visual analytics tool detects and corrects concept drift in data streams.
problem Concept drift causes inaccurate predictions in evolving data.
method DriftVis combines drift detection and visualization.
result Visual analytics supports detection, examination, and correction of concept drift.
The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have bee…
FeatureEnVi aids in feature engineering with visual analytics.
problem Insufficient support for feature engineering in visual analytics tools.
method Stepwise selection and semi-automatic extraction approaches.
result Extracts heavily engineered features evaluated by multiple metrics.
We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is…
This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, except for internal communication as in any orga…
Regshock visualizes financial risks to help regulators manage systemic shocks.
problem Managing systemic risks in financial networks.
method Risk-island visualization algorithm and regshock visual exploration approach.
result Demonstrated improved risk management and control capabilities.
It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing pr…
Study evaluates interpretability of time series foundation models' latent spaces.
problem Improving interpretability of latent spaces in time series models for visual analytics.
method Evaluated MOMENT family of transformer-based models on five datasets, fine-tuning for performance.
result Fine-tuning improved latent space clarity but limited interpretability remained.
Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of…
TS-Insight visualizes Thompson Sampling for better debugging and trust.
problem Thompson Sampling's black box nature hinders debugging and trust.
method TS-Insight is a visual analytics tool that traces evolving posteriors and evidence counts.
result Visualizations help in verifying, diagnosing, and explaining Thompson Sampling dynamics.
Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are…
Visual analytics system for comparing medical records using sequence embeddings.
problem Challenges in analyzing medical records due to high dimensionality, irregularity, and sparsity.
method Event and sequence embeddings using autoencoder and self-attention mechanism, with sequence alignment for comparison.
result Demonstrated effectiveness with real-world neonatal ICU dataset.
The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demogra…
As machine learning becomes more pervasive, there is an urgent need for interpretable explanations of predictive models. Prior work has developed effective methods for visualizing global model behavior, as well as generating local (instance-specific) explanations. However, relatively little work has addressed regional …
MetaStackVis aids in choosing better metamodels for stacking ensembles.
problem Difficulty in selecting optimal metamodels for stacking ensembles.
method Interactive visualization tool to explore and compare different metamodels.
result Alternative metamodels significantly improve stacking ensemble performance.
Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics has led to a wide …
VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Treescs.LG
VisRuler simplifies decision extraction from bagged and boosted trees.
problem Complexity and lack of interpretability in ensemble models.
method Visual analytics tool for selecting robust models, important features, and essential decisions.
result Users successfully extracted and explained decisions from ensemble models.
New report on machine learning visualization techniques and trends.
problem Improving trust in machine learning models through visualization.
method Analysis of peer-reviewed articles on machine learning visualization techniques.
result Rapid growth in machine learning visualization techniques over the past three years.
HardVis helps visually manage imbalanced data by sampling hard instances.
problem Managing unsafe or noisy instances in imbalanced classification.
method Visual analytics system using undersampling and oversampling techniques.
result Boosts predictive power of ML models by balancing data sets.
Synthetic construction of Hopf fibration in 4D space.
problem Visualizing 4D objects in 3D space.
method Double orthogonal projection method to visualize 4D space.
result Direct synthetic construction of 3-sphere fibers from 2-sphere points.
Sphere eversions have been described so far by either pictures with minimal topological complexity, numerical evolution or complex equations. We write down relatively simple explicit formulas for the whole eversion, both analytic and topologically simpler, including also Boy surface (real projective plane), using a fam…
The paper offers a checklist for comparing human and machine visual perception.
problem Comparing human and machine visual perception.
method Designing, conducting, and interpreting experiments to investigate mechanisms.
result Feedback mechanisms may not be necessary for visual reasoning tasks.
InstanceFlow visualizes classifier confusion over training epochs.
problem Limited model interpretability through aggregate performance measures.
method Dual-view visualization tool showing instance-level learning behavior.
result Allows temporal analysis of training process and instance-level performance.
VisEvol uses evolutionary optimization to find optimal hyperparameters for machine learning models.
problem Finding the best hyperparameters for complex machine learning models is computationally intensive and challenging.
method VisEvol employs evolutionary optimization, storing performant models and improving others through crossover and mutation processes.
result VisEvol generates a voting ensemble of models with improved predictive performance.
Efficiently visualizes uncertainty in local divergence of 2D vector fields.
problem Uncertainty in vector field data leads to inaccurate divergence computations.
method Closed-form approach for highly efficient and accurate uncertainty visualization of local divergence, assuming independently Gaussian-distributed vector uncertainties.
result Significantly enhanced efficiency and accuracy of our algorithms over classical MC approach.
A method to visualize multidimensional local subspaces using implicit differentiation.
problem Understanding the effect of multidimensional projection on local subspaces.
method Implicit function differentiation to analyze local subspaces shaped by multidimensional ellipses.
result Visualization of local subspaces provides insights into the global structure of data.
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscs.LG
Report on enhancing trust in ML models with visualizations.
problem Understanding and trusting the results of black box ML models.
method Categorization of visualization techniques, statistical overview, topic analyses, and interactive web-based survey.
result Expanded categorization of trust against different facets of interactive ML.
Automating the customer analytics process is crucial for companies that manage distinct customer bases. In such data-rich and dynamic environments, visualization plays a key role in understanding events of interest. These ideas have led to the popularity of analytics dashboards, yet academic research has paid scant att…
We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We descr…
Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requireme…
Study improves document processing in banking with multimodal analytics.
problem Raising operational efficiency in banking through document-intensive processes.
method Comparative analysis of text classifiers and multimodal model (LayoutXLM) on company register extracts.
result Incorporating layout information in a model substantially increases performance.
Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, expert systems, statistics, and operations research. This paper provides an integrative review at the boundary of these areas. The aim is to give researchers in the intelligen…
We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, depend…
Visualizes classification results with class maps.
problem Label bias in classification predictions.
method Class maps reflecting probability, distance, and mislabeling likelihood.
result Insight into classification results and data structure.
StackGenVis simplifies ensemble learning by visualizing model selection and performance.
problem Complexity in choosing and combining models for stacking ensemble learning.
method Visual analytics system that dynamically adapts performance metrics, manages data instances, selects algorithms, and measures predictive performance.
result Reduces complexity of stacking by removing overpromising and underperforming models.
This paper introduces TDA and TSI for better business analytics.
problem Nonlinear, multi-scale business datasets under-represented by traditional tools.
method Topological Data Analysis (TDA) and Topological Stability Index (TSI).
result TSI reveals structural variability in business data.
Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the fiel…
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of obser…
New method makes quality metrics scale-invariant for high-dimensional data.
problem Scale sensitivity in quality metrics affects the accuracy of data projections.
method Analytical and empirical investigation of stress and KL divergence; introduction of a scale-invariant technique.
result The proposed technique accurately captures expected behavior and makes metrics scale-invariant.
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary cl…
GraphQ system uses GNNs to search for subgraph patterns in graphs.
problem Efficiently identifying and matching subgraph patterns in graph data.
method Graph neural networks (GNNs) for encoding graph data and NeuroAlign for node alignment.
result NeuroAlign improves node-alignment accuracy by 19-29% compared to baseline GNNs.
Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates e…