Balance data using different criteria

Question

I have a dataset of audio and text files that I want to balance using different criteria to train a neural network. The text and matching audio file are grouped under one ID.

For each ID, I have a number of words and some metadata.

Let's say my data have three metadata/criteria that need to be balanced (well... kind of, I want to be able to choose the percentage for some criteria values) :

• The name of the speaker, each speaker should have approximately the same number of words.
• The status of the speaker (active if the speaker is still in activity or inactive if not), where I want approximately 70 % active speakers and 30 % inactive.
• The gender of the speaker, where I want approximately 50 % male and 50 % female.

Example : ID12345 with 10,000 words. Metadata : John, male and active

If I balance one of the criteria, then another, I will surely unbalance the previously balanced one. Moreover, if I balance using only one criterion at a time, I could throw away data that could be useful to the balance of the following criteria, which mean I will end up with less data that I could have had optimally.

Is there an algorithm that could balance the data using all criteria with their percentage target, and maximise the number of words remaining ?

I'm looking for a general algorithm, with a variable number of criteria.

EDIT : I use C++ and MongoDB (but I'll accept other languages/tools if it can help me, as well as algorithms).

Answer 1

You would need to balance the data according to the joint distribution of all 3 features.

One simple option is to treat each feature as categorical (binning any continuous features), then map each combination of features to a single new feature:

doc_len_tokens  | status   | gender | level
----------------|----------|--------|------
1               | active   | male   | 1
1               | active   | female | 2
1               | inactive | male   | 3
1               | inactive | female | 4
2               | active   | male   | 5
2               | active   | female | 6
2               | inactive | male   | 7
2               | inactive | female | 8
... etc ...

You would then stratify on level when sampling.

Here is an example in SQL:

SELECT
    data.unique_id,
    data.doc_num_tokens,
    data.status,
    data.gender
FROM (
    SELECT
        data.unique_id,
        data.doc_num_tokens,
        data.status,
        data.gender,
        row_number() OVER (
            PARTITION BY
                data.doc_num_tokens,
                data.status,
                data.gender
            ORDER BY
                newid()
        ) AS row_num
    FROM
        my_data AS data
) AS data
WHERE
    row_num <= ${number of rows to sample}