Hi, T have very little experience with Logic based Al algorithms, and my understanding is these machines use propositional logic algorithms (which wasn’t something I have come across before, even when I was in uni).
My experience is in statistical & neural based networks and can understand the pros and cons of those networks.
I am just trying to understand what is the advantage and disadvantages of logic based algorithms specifically Tsetlin Machines? Is this something that I should learn more about? What are some good resources?
Thanks
I actually stumbled over your research by accident when searching for a job. Pretty interesting model!
When is the Timeserie chapter for your book coming out?
Thanks, u/JustAnotherRedUser1 - the convolution chapter is coming next, in a few weeks. After that, the one on regression. Aim to complete the book in the next six months, so time series will be covered sometime before that.
Looking forward to it, great job so far!
I have a question about applying test sets to models. Although I planned to email you, but i found you here by random!
My master’s thesis in Finance was based on Easley et al.'s 2020 paper “Microstructure in the Machine Age” from the Review of Financial Studies. This paper used Random Forest (RF) to analyze financial market microstructures. The researchers created a RF-model with dataset of several microstructure indicator. Then evaluated which features were most significant in classifying the new unseen data (test-set). We do not look at prediction in this case, we look at the which features split the most effectively on the test-set.
In our tests, several indicators had high feature importance when creating the RF-model. But when applied to new, unseen data (the test set), the importance of these features often changed. Features that were crucial in the training phase were not as important for splitting new data, a pattern we consistently observed.
Easley et al. (2020) suggest that this occurs because financial microstructures are traditionally constructed on “in-sample” data, which has limited predictive power for new, unseen data.
I believe this finding can be important, as it can serve as an alternative way to back-testing. As backtesting does not make any sense for financial data, as all the random states in the market has already known.
I’m wondering if the Tsetlin Machine could be used to measure and analyze the same idea?
Sounds like an exciting problem! I guess leveraging the Tsetlin machine clauses can give a fresh take on the task. Tsetlin machines also support reasoning by elimination. That is, it can learn what the target isn’t instead of what it is, for increased robustness: https://www.ijcai.org/proceedings/2022/616