Classify sequence of flags

Question

I am not able to find an answer to how I should classify a varying number of sequence of binary flags + other features. My data looks like this (these are events, so the order is important and I may have other features in addition to sequence):

ID	Flag 1	Flag 2	Flag 3	Other Feature
A	1	1	0	0.1
A	0	1	0	0.3
A	0	0	1	3.1
B	0	1	0	1.1
B	1	1	0	0.0

Notice that ID:B does not have the same number of entries (only 2). Any suggestion on how I should organize this data and what should I use to classify? How do I better capture the sequence of Flags? During inference, I will provide the sequence of flags [[1,1,0],[0,1,0],[0,0,1]] OR [[0,1,0],[1,1,0]] and "other feature" to get the label since the order of sequence makes up the positive or negative label.

Answer 1

You have a classification problem over a time-series, basically. You need to group all the events with the same ID under a single sequence, like [A, A, A] (is one sample) and [B,B] (is another).

Your targets ($y$) will be the class label, for each sequence and each element of the sequence. Then the $x$ will comprise the three flags and "other feature(s)" you have.

You can tackle the problem with three kinds of neural networks: 1D CNNs (convolutional nets), RNNs (recurrent nets), and even transformers (i.e. attention-based models).

• Since you have sequences of the kind $(B,T,N)$ - where $B$ is the batch size, $T$ the num of events with same ID (you may need to zero-pad), and $N$ the number of features - and have a target for each $t\in T$, you need to predict the class label for each timestep $T$, and each sequence in $B$.
• So you're output won't be a single value for one input sequence but a sequence of values of size $T$.