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How to optimize NN seq2seq classification training with time-dependent class imbalance?

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0

$begingroup$

I want to classify timeseries using seq2seq since they have a high chance of being "corrupted" towards the end.

The traces are labelled e.g.

111111100000
555500000000
330000000000
444444440000
444444444444

as you can see, they (almost) always end up in zero-land.

If I plot the label-distribution over time, it looks something like the plot below, where the trace has an increasingly growing chance of being terminated and zero-ed at some point.

Are there any common methods for balancing problems like this, or should I simply try to balance classes based on e.g. the first n datapoints only?

neural-network deep-learning classification sequence-to-sequence

share|improve this question

asked Mar 18 at 17:45

komodovaran_komodovaran_

1114

$endgroup$

add a comment | 

0

$begingroup$

I want to classify timeseries using seq2seq since they have a high chance of being "corrupted" towards the end.

The traces are labelled e.g.

111111100000
555500000000
330000000000
444444440000
444444444444

as you can see, they (almost) always end up in zero-land.

If I plot the label-distribution over time, it looks something like the plot below, where the trace has an increasingly growing chance of being terminated and zero-ed at some point.

Are there any common methods for balancing problems like this, or should I simply try to balance classes based on e.g. the first n datapoints only?

neural-network deep-learning classification sequence-to-sequence

share|improve this question

asked Mar 18 at 17:45

komodovaran_komodovaran_

1114

$endgroup$

add a comment | 

$begingroup$

I want to classify timeseries using seq2seq since they have a high chance of being "corrupted" towards the end.

The traces are labelled e.g.

111111100000
555500000000
330000000000
444444440000
444444444444

as you can see, they (almost) always end up in zero-land.

If I plot the label-distribution over time, it looks something like the plot below, where the trace has an increasingly growing chance of being terminated and zero-ed at some point.

Are there any common methods for balancing problems like this, or should I simply try to balance classes based on e.g. the first n datapoints only?

neural-network deep-learning classification sequence-to-sequence

share|improve this question

asked Mar 18 at 17:45

komodovaran_komodovaran_

1114

$endgroup$

111111100000
555500000000
330000000000
444444440000
444444444444

share|improve this question

asked Mar 18 at 17:45

komodovaran_komodovaran_

1114

share|improve this question

asked Mar 18 at 17:45

komodovaran_komodovaran_

1114

asked Mar 18 at 17:45

komodovaran_komodovaran_

1114

asked Mar 18 at 17:45

komodovaran_komodovaran_

1114