CLASSify: A Web-Based Tool for Machine Learning

• URL: http://arxiv.org/abs/2310.03618v1
• Date: Thu, 5 Oct 2023 15:51:36 GMT
• Title: CLASSify: A Web-Based Tool for Machine Learning
• Authors: Aaron D. Mullen, Samuel E. Armstrong, Jeff Talbert, V.K. Cody Bumgardner
• Abstract summary: This article presents an automated tool for machine learning classification problems to simplify the process of training models and producing results while providing informative visualizations and insights into the data. We present CLASSify, an open-source tool for solving classification problems without the need for knowledge of machine learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning classification problems are widespread in bioinformatics, but the technical knowledge required to perform model training, optimization, and inference can prevent researchers from utilizing this technology. This article presents an automated tool for machine learning classification problems to simplify the process of training models and producing results while providing informative visualizations and insights into the data. This tool supports both binary and multiclass classification problems, and it provides access to a variety of models and methods. Synthetic data can be generated within the interface to fill missing values, balance class labels, or generate entirely new datasets. It also provides support for feature evaluation and generates explainability scores to indicate which features influence the output the most. We present CLASSify, an open-source tool for simplifying the user experience of solving classification problems without the need for knowledge of machine learning.

Related papers

• RESTOR: Knowledge Recovery through Machine Unlearning [71.75834077528305]
  Large language models trained on web-scale corpora can memorize undesirable datapoints. Many machine unlearning methods have been proposed that aim to 'erase' these datapoints from trained models. We propose the RESTOR framework for machine unlearning based on the following dimensions.
  arXiv  Detail & Related papers  (2024-10-31T20:54:35Z)
• Verification of Machine Unlearning is Fragile [48.71651033308842]
  We introduce two novel adversarial unlearning processes capable of circumventing both types of verification strategies. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
  arXiv  Detail & Related papers  (2024-08-01T21:37:10Z)
• Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach [50.36650300087987]
  This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism. We have found that integrating the forgetting mechanisms significantly enhances the models' performance in acquiring new knowledge.
  arXiv  Detail & Related papers  (2024-03-27T05:10:38Z)
• Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools [0.0]
  Automated Machine Learning (AutoML) streamlines the machine learning model development process. This democratization allows more users (including non-experts) to access and utilize state-of-the-art machine-learning expertise. However, AutoML tools may also propagate bias in the way these tools handle the data, model choices, and optimization approaches adopted.
  arXiv  Detail & Related papers  (2023-12-16T19:54:00Z)
• What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components [77.87794937143511]
  This paper introduces a collection of hands-on training materials for explaining data-driven predictive models. These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation.
  arXiv  Detail & Related papers  (2022-09-08T13:33:25Z)
• Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
  Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
  arXiv  Detail & Related papers  (2022-09-05T16:48:34Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)
• Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning [9.13755431537592]
  Iterative Visual Logical (IVLC) is an interpretable machine learning algorithm. IVLC is especially helpful when dealing with sensitive and crucial data like cancer data in the medical domain. This chapter proposes an automated classification approach combined with new Coordinate Order (COO) algorithm and genetic algorithm.
  arXiv  Detail & Related papers  (2021-07-11T05:39:14Z)
• Industrial Machine Tool Component Surface Defect Dataset [0.3170655320696991]
  Using machine learning (ML) techniques in general and deep learning techniques in specific needs a certain amount of data. The manual inspection of machine tool components and the manual end-of-line check of products are labor-intensive tasks. One needs real-world datasets to train and test the models.
  arXiv  Detail & Related papers  (2021-03-24T06:17:21Z)
• An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges [11.329636084818778]
  In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. We present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. A solution is proposed for each task employing autoencoders as the only learning method.
  arXiv  Detail & Related papers  (2020-05-21T08:41:57Z)
• Visualisation and knowledge discovery from interpretable models [0.0]
  We introduce a few intrinsically interpretable models which are also capable of dealing with missing values. We have demonstrated the algorithms on a synthetic dataset and a real-world one.
  arXiv  Detail & Related papers  (2020-05-07T17:37:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.