A survey of visual analytics techniques for machine learning

Yuan, Jun; Chen, Changjian; Yang, Weikai; Liu, Mengchen; Xia, Jiazhi; Liu, Shixia

doi:10.1007/s41095-020-0191-7

Cited by 189 publications

(69 citation statements)

References 265 publications

(153 reference statements)

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“…In the field of visualization, many methods have been proposed to improve data quality. Based on whether the data is labeled, the relevant work can be classified into two categories: improving the quality of noisy labeled samples and unlabeled samples [10].…”

Section: Related Workmentioning

confidence: 99%

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Chen

Wang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Section: Related Workmentioning

confidence: 99%

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Chen

Wang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…Yuan et al [19] reviewed techniques of visual analytics for machine learning by categorising them into techniques before model building, techniques during modeling building, and techniques after model building. Chatzimparmpas et al [20] investigated approaches of enhancing trust in ML models with the use of interactive visualization.…”

Section: Visualisation In Explanation Of Machine Learningmentioning

confidence: 99%

Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation

2021

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Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare a substantial amount of generated ML models to find the optimal one for deployment. It is challenging to compare such models with a dynamic number of features. Comparison is more than only finding differences of ML model performance, as users are also interested in the relations between features and model performance such as feature importance for ML explanations. This paper proposes RadialNet Chart, a novel visualisation approach, to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In RadialNet Chart, ML models and features are represented by lines and arcs, respectively. These lines are generated effectively using a recursive function. The dependence of ML models with a dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in RadialNet Chart. Taken together with the structure of visualisation, feature importance can be directly discerned in RadialNet Chart for ML explanations. Compared with other commonly used visualisation approaches, RadialNet Chart can help to simplify the ML model comparison process with different benefits such as the following: more efficient in terms of helping users to focus their attention to find visual elements of interest and easier to compare ML performance to find optimal ML model and discern important features visually and directly instead of through complex algorithmic calculations for ML explanations.

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“…Visualization has become an increasingly important research area due to its wide range of applications in many disciplines [19,23,25,26,31,38,41,[53][54][55][56][57][58][59][60]62]. Recently, the visualization and visual analytics community is eliciting considerable interests in blockchain data presentation and analysis, leading to several pioneering studies [45].…”

Section: Blockchain Data Visualizationmentioning

confidence: 99%

SilkViser: A Visual Explorer of Blockchain-based Cryptocurrency Transaction Data

Zhao¹

2020

Preprint

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<p>Many blockchain-based cryptocurrencies provide users with online blockchain explorers for viewing online transaction data. However, traditional blockchain explorers mostly present transaction information in textual and tabular forms. Such forms make understanding cryptocurrency transaction mechanisms difficult for novice users (NUsers). They are also insufficiently informative for experienced users (EUsers) to recognize advanced transaction information. This study introduces a new online cryptocurrency transaction data viewing tool called SilkViser. Guided by detailed scenario and requirement analyses, we create a series of appreciating visualization designs, such as paper ledger-inspired block and blockchain visualizations and ancient copper coin-inspired transaction visualizations, to help users understand cryptocurrency transaction mechanisms and recognize advanced transaction information. We also provide a set of lightweight interactions to facilitate easy and free data exploration. Moreover, a controlled user study is conducted to quantitatively evaluate the usability and effectiveness of SilkViser. Results indicate that SilkViser can satisfy the requirements of NUsers and EUsers. Our visualization designs can compensate for the inexperience of NUsers in data viewing and attract potential users to participate in cryptocurrency transactions.</p>

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A survey of visual analytics techniques for machine learning

Cited by 189 publications

References 265 publications

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation

SilkViser: A Visual Explorer of Blockchain-based Cryptocurrency Transaction Data

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