Answers.Body: <p>If you are gearing towards publishing, I would recommend the following.</p>
  <ol>
  <li>There are different ML journals and conferences. Pick what suits you. Conference publications are generally quick compared to Journals. You can refer sample papers from different sources and decide what you would like. In the world of ML, researchers opt for conferences because it allows you to get your findings out to public more quickly compared to journals.</li>
  <li>Once you decide your publication medium get some sample papers that have been already published there and try to follow a style similar. Beware writing publication material is hard and will take many iterations.</li>
  <li>Write up the theoretical background of the algorithm with required mathematical / analytical proofs.</li>
  <li>Generate baseline comparison results with benchmark datasets. Eg. if your algorithm is for tabular datasets, use standard MNIST, IRIS etc datasets to have a baseline comparison with other algorithms which have their results published. (Choose those based on what your algo does - classification or regression).</li>
  <li>Once you have good benchmark results, compile them, write a detailed discussion on what worked and what did not with explanations.</li>
  <li>Remember to put all necessary material publicly (eg. Github) available to reproduce your results.</li>
  </ol>
  <p>For any research paper to be accepted for publication, the method need to be novel with concise proofs and explanations. You should be able to clearly explain and establish the novelty and superiority of the method with sufficient experimental proof.</p>
  <p>These are general directions, but I would highly recommend talking to someone in person who has already published research.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-06-29T12:46:04.550Z
Answers.LastActivityDate: 2022-06-29T12:46:04.550Z
Answers.UserDisplayName: Arun Aniyan
Body: <p>I designed a ML classification algorithm that's simple, efficient, and effective. It's not perfect, but seems to be widely applicable across domains.</p>
  <p>I'd like to submit it for publication, but I don't know how to present my case.</p>
  <p>What does an ML research paper need to succeed through peer review to be published? Further, what does it need to be noticed by the ML community as a whole?</p>
  
Tags: <neural-networks><machine-learning><classification><research>
Title: How can I demonstrate a novel ML classification algorithm has value?
UserDisplayName: J Doug
ThreadText: # How can I demonstrate a novel ML classification algorithm has value?
  
  (Asked by: username_0 on 2022-06-27)
  
  I designed a ML classification algorithm that's simple, efficient, and effective. It's not perfect, but seems to be widely applicable across domains.
  
  I'd like to submit it for publication, but I don't know how to present my case.
  
  What does an ML research paper need to succeed through peer review to be published? Further, what does it need to be noticed by the ML community as a whole?
  
  **Tags:** neural-networks, machine-learning, classification, research
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-06-29. Score: 1
  
  If you are gearing towards publishing, I would recommend the following.
  
  1. There are different ML journals and conferences. Pick what suits you. Conference publications are generally quick compared to Journals. You can refer sample papers from different sources and decide what you would like. In the world of ML, researchers opt for conferences because it allows you to get your findings out to public more quickly compared to journals.
  2. Once you decide your publication medium get some sample papers that have been already published there and try to follow a style similar. Beware writing publication material is hard and will take many iterations.
  3. Write up the theoretical background of the algorithm with required mathematical / analytical proofs.
  4. Generate baseline comparison results with benchmark datasets. Eg. if your algorithm is for tabular datasets, use standard MNIST, IRIS etc datasets to have a baseline comparison with other algorithms which have their results published. (Choose those based on what your algo does - classification or regression).
  5. Once you have good benchmark results, compile them, write a detailed discussion on what worked and what did not with explanations.
  6. Remember to put all necessary material publicly (eg. Github) available to reproduce your results.
  
  For any research paper to be accepted for publication, the method need to be novel with concise proofs and explanations. You should be able to clearly explain and establish the novelty and superiority of the method with sufficient experimental proof.
  
  These are general directions, but I would highly recommend talking to someone in person who has already published research.
  
  
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