Literature DB >> 29177022

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.

Gang Luo1.   

Abstract

For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic.

Entities:  

Keywords:  Machine learning; automatic administration; data mining; load management; progress indicator

Year:  2017        PMID: 29177022      PMCID: PMC5699516          DOI: 10.1145/3166054.3166057

Source DB:  PubMed          Journal:  SIGKDD Explor        ISSN: 1931-0145


  4 in total

1.  Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

Authors:  Xueqiang Zeng; Gang Luo
Journal:  Health Inf Sci Syst       Date:  2017-09-27

2.  Predicting sample size required for classification performance.

Authors:  Rosa L Figueroa; Qing Zeng-Treitler; Sasikiran Kandula; Long H Ngo
Journal:  BMC Med Inform Decis Mak       Date:  2012-02-15       Impact factor: 2.796

3.  PredicT-ML: a tool for automating machine learning model building with big clinical data.

Authors:  Gang Luo
Journal:  Health Inf Sci Syst       Date:  2016-06-08

4.  Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

Authors:  Gang Luo; Bryan L Stone; Michael D Johnson; Peter Tarczy-Hornoch; Adam B Wilcox; Sean D Mooney; Xiaoming Sheng; Peter J Haug; Flory L Nkoy
Journal:  JMIR Res Protoc       Date:  2017-08-29
  4 in total
  3 in total

1.  Progress Indication for Machine Learning Model Building: A Feasibility Demonstration.

Authors:  Gang Luo
Journal:  SIGKDD Explor       Date:  2018-12

2.  Progress Indication for Deep Learning Model Training: A Feasibility Demonstration.

Authors:  Qifei Dong; Gang Luo
Journal:  IEEE Access       Date:  2020-04-22       Impact factor: 3.367

3.  Improving the Accuracy of Progress Indication for Constructing Deep Learning Models.

Authors:  Qifei Dong; Xiaoyi Zhang; Gang Luo
Journal:  IEEE Access       Date:  2022-06-08       Impact factor: 3.476

  3 in total

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