So far we have seen a meta-learning example with an image dataset (e.g., Omniglot) that has only one attribute. However, they have multiple attributes if we want to use non-image datasets (e.g., diabetes prediction based on IoT sensors). If we want to generate tasks based on various datasets with different attributes, there is no good reference. How to convert that into the n-way k-shot format with multiple attributes?
I really appreciate any help you can provide.
Handling datasets with multiple attributes in meta-learning can be more challenging than dealing with datasets with only one attribute. Here are some steps you can follow to convert a dataset with multiple attributes into the n-way k-shot format for meta-learning:
Overall, the process of converting a dataset with multiple attributes into the n-way k-shot format for meta-learning requires careful consideration of the attributes, task generation, and encoding. It is essential to choose the right set of attributes and generate tasks that are representative of the data distribution to obtain good results.
Dear Anik Islam Abhi ,
Choosing the most suitable algorithm to perform a machine learning task for a new problem is a recurrent and complex task. In multi-target regression tasks, when problem transformation methods are applied, this choice is even harder. The reason is the need to simultaneously choose the problem transformation method and the base learning algorithm.
https://www.sciencedirect.com/science/article/abs/pii/S0020025521011130
Regards,
Shafagat
Dear Professor Shafagat Mahmudova ,
Thank you for sharing such an interesting article. They seem used machine learning. I am thinking to use deep learning. Though data handling can be the same. The paper is a paid article. Is it possible to share the pdf in a private message or here?
I really appreciate any help you can provide.
Dear Najla Matti Isaacc ,
Thank you for sharing such interesting tips. Sure, I will follow that.
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