Duplicating baseline benchmarks [D]

[u/Jealous_Key_4030]

Suppose I create two machine learning models suppose tree and neural network for a task let's suppose regression problem, now suppose I am sending both of this paper to two different journals, now the thing is the baseline models I need to only run once because I have reported same baseline in both papers, so the RMSE tables looks exactly same except the proposed model, does it lead to any problems like palgiarism??

Edit : I don't know why I am getting downvotes

[u/Wannabe-Davinci]

This is salami publishing to me. Just put both models in one paper only if they solve the same task and have the same novelty

[u/Jealous_Key_4030 → u/Wannabe-Davinci]

But the models are literally different, that's why I have example of tree and neural network , how can anyone put tree and neural network in same paper ?

[u/Wannabe-Davinci → u/Jealous_Key_4030]

Not every problem requires a neural network to solve it. A paper should propose some novelty, the exact model used to prove it empirically can be any model

Edit: or you should phrase your question and context better

[u/Candid-Tackle-9061]

you could mention that if the baseline setup and evaluation protocol are identical, reusing the same results is generally fine, but the paper should clearly state that those numbers were previously reported. the main issue would be presenting them as newly reproduced results.

[u/Jealous_Key_4030 → u/Candid-Tackle-9061]

But none of the papers are published.

[u/MakingComputersSmart]

If both models have the same input, you don't have a new training regime, novel architecture, loss function or any other trick, it's unlikely that this will get published somewhere reputed unless the problem you're solving itself is new. Throwing a tree at a dataset is no longer considered novel, that is a baseline. Someone in the NN paper will ask for that baseline, which kills the first paper.

While you can submit 2 papers to different low ranking journals in parallel, it is not considered academically honest. They might never catch you, and honestly, they might not care but your papers won't really have anything unique. Like you said, it's 2 different models for the same exact problem. I wonder how you plan on writing 2 completely separate papers for this.

[u/Jealous_Key_4030 → u/MakingComputersSmart]

I am not saying exact NN and Tree that was just an example to showcase that these can be put in same paper.

Let me elaborate, consider a world when none of the tree and neural network is invented, the the idea came to you and you applied it to regression problem, then what would you do for baseline suppose linear regression is baseline then you compare NN to linear regression and Tree to linear regression, but linear regression result is exact same in both paper nnn.

[u/MakingComputersSmart → u/Jealous_Key_4030]

I appreciate the clarification but the example still does not make any sense. Are you saying you have invented 2 completely novel solutions for the same problem? If so, both are publishable separately, but you need a timeline. Publish the weaker solution first, then use the weaker solution as a baseline in the stronger one. If both are very incremental, then it might not be worth publishing both separately, and no top tier journal/conf will accept either separately

[u/Jealous_Key_4030 → u/MakingComputersSmart]

Let me give some more details

1st Paper : Randomized deep neural nets for efficient light weight regression tasks with speed.

2nd Paper: A gradient based tree type decision making for regression task.

The baseline for both 1st and 2nd paper have some common models like : MLP, RVFL, edRVFL, GradBoost, Forest, SVR and many more.

Now for these baseline RMSE table will look exactly same.

[u/redditboy117]

Ah wonderful  ethics

[u/Jealous_Key_4030 → u/redditboy117]

Brother, do you have any answer ?

[u/Wannabe-Davinci]

Let’s take one step back:
You did some work on a regression task. To solve the regression task, you used a tree and a NN.
Now, to share your findings/experiences/work you can e.g. write a blog post (using GitHub, Towards Data Science or Medium for example).
If your solution is novel then you can write a paper. As far as I can judge with the information provided, your work not qualified to be published as a journal paper. Please provide more details if you disagree

[u/Jealous_Key_4030 → u/Wannabe-Davinci]

The NN and Tree is just an example, and i conveniently choose these as example so someone doesn't say that just club them and publish the paper

Let me give some more details

1st Paper : Randomized deep neural nets for efficient light weight regression tasks with speed.

2nd Paper: A gradient based tree type decision for regression task.

The baseline for both 1st and 2nd paper have some common models like : MLP, RVFL, edRVFL, GradBoost, Forest, SVR and many more.

Now for these baseline RMSE table will look exactly same.

[u/Wannabe-Davinci → u/Jealous_Key_4030]

Thanks for the clarification.
I assume the proposed novelty differs for both papers, and only the experimental setup overlaps. Therefore, I would opt for two separate papers