I'm currently trying to classify discrete sequential data into five classes with machine learning.
The setup is the following: The actual object is filled with various properties, but to separate the objects and assign them to a class, it's necessary to look at the pattern of occurrence over a year. Therefore, I convert the objects to an array of 0- and 1's with a fixed length of 366.
Example: If the objects occur on the 1st and 3rd of January, the first elements of the sequence will look like this: [1, 0, 1, ... ]
So the data for each object would be a sequence of the fixed length of 366 and could look like this:
So the goal would be to enter the sequences into a model to classify its class.
First question: I tried this pattern classification with a CNN and achieved an accuracy of up to 80%. But are there any other approaches to tackle this problem? It seems like a simple problem, so I'd expect a simple solution.
Second question: Let's say I wanted only to identify one of those classes. What should the setup look like? Just input data of that class?