DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting

Dong, Sun; Feng, Zezheng; Chen, Yuanzhe; Wang, Yong; Zeng, Jia; Yuan, Mingxuan; Pong, Ting-Chuen; Qu, Huamin

doi:10.1145/3313831.3376866

Cited by 24 publications

(19 citation statements)

References 59 publications

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“…Dove et al [209] surveyed to understand how design innovation is practiced in the ML domain in terms of user experience. [63], [70], [165], [174], [211] [124], [125], [128], [175], [217] [69], [112], [113], [118], [130], [131], [133], [135], [137], [140], [144], [152], [153], [155] [49], [57], [67], [98], [110], [114], [180], [192], [193], [203], [214], [215] [48], [59], [62], [75], [79], [111], [120], [156], [159], [185], [195], [197], [207], [218] [65], [83],…”

Section: Survey Resultsmentioning

confidence: 99%

“…Iconate [ 119 ] is a tool created to generate complex icons based on text queries. Sun et al [ 120 ] present a system to aid model selection for demand forecasting. Im et al [ 121 ] attempted to create an application to derive social signals for online social platforms computationally.…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

“…Some of the prior works that target the developer as the human focus on novice ML engineers to help them develop ML systems faster [62,197]. Notably, the majority of works that target the developer side focused on ML engineers [59,65,71,75,79,120,156,160,185,195,196,198,199,201,207].…”

Section: The 'Human' In Hcmlmentioning

confidence: 99%

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Section: Survey Resultsmentioning

confidence: 99%

Section: Classifying Hcml Researchmentioning

confidence: 99%

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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“…However, demand forecasting models are facing the challenge of market uncertainty and data complexity. In this regard, the study presented in [47] focused on developing a VA system to demonstrate the performance of various models in order to support decision makers in selecting the best performing model for their demand forecasting activities. Both manufacturers and retailers can benefit from such models towards facilitating their model selection tasks.…”

Section: ) Plan Processesmentioning

confidence: 99%

Visual Analytics for Decision Support: A Supply Chain Perspective

2021

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Supply chain (SC) activities generate huge amount of data that can be used in decision making processes. However, proper data analytics techniques are required to combine, organize, and analyze data from different sources and produce required insights available for decision makers. These techniques promote analytical reasoning of the events and patterns hidden in the data using visualizations, so-called Visual Analytics (VA). Although there is a large number of VA systems to facilitate the process of analysis and decision making, there is a lack of an adequate overview of what already exists in this area for SC management. To address that need, we conducted a systematic literature review to analyze the state of the art in SC VA systems. Particularly, we focus on use cases, the type of the decisions that a VA system intended to support, the type of visualizations employed, the type of analytics used, and the data that has been used for analysis. The goal of this study is to provide SC and VA researchers with an overview of the works carried out in the field of SC VA, helping them to observe latest trends and to recognize existing gaps that need further investigation. Consequently, a mapping between decisions of various SC business processes and their reciprocal visualization techniques and tactics have been provided. Adding to that, VA applications and use cases in SC are identified based on the SC Operation Reference (SCOR) model and underlying decision areas are recognized.

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“…Most AutoML systems use performance metrics as basis for model evaluation and ranking. However, research on model analytics and comparison of manually-built models has shown that none of the stakeholders are satisfied to only see performance metrics [21,23,28,29,35]. They are interested in details such as types of errors, how models perform on specific instances, and a detailed reasoning by which models make predictions.…”

Section: Introductionmentioning

confidence: 99%

Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

Narkar

Zhang²,

Liao³

et al. 2021

26th International Conference on Intelligent User Interfaces

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Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models. A critical, final step of AutoML is human selection of a final model from dozens of candidates. In current AutoML systems, selection is supported only by performance metrics. Prior work has shown that in practice, people evaluate ML models based on additional criteria, such as the way a model makes predictions. Comparison may happen at multiple levels, from types of errors, to feature importance, to how the model makes predictions of specific instances. We developed Model LineUpper to support interactive model comparison for AutoML by integrating multiple Explainable AI (XAI) and visualization techniques. We conducted a user study in which we both evaluated the system and used it as a technology probe to understand how users perform model comparison in an AutoML system. We discuss design implications for utilizing XAI techniques for model comparison and supporting the unique needs of data scientists in comparing AutoML models. CCS CONCEPTS• Human-centered computing → Empirical studies in visualization; • Computing methodologies → Machine learning.

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DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting

Cited by 24 publications

References 59 publications

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Visual Analytics for Decision Support: A Supply Chain Perspective

Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

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