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Every state has its own sub-attributes, for example,

state S{ATTR1, ATTR2, ATTR3}.

We can train the model use data with states filled with all three attributes. When we want to predict, we just want to use a subset of the attributes, like

S{ATTR1, ATTR2}, ATTR3 is ignored,

When decoding/recognizing, is there an appropriate model for to use?

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Yes this is exactly the same what is done in supervised learning. We extract $N$ features and they correspond to $N$ attributes in your case. Then using a classifier such as Mahalonobis or Support vector machines (SVM) we perform the test. If we believe that only the first three attributes are meaningful then we can simply fill the last attribute with ones or zeros. Then SVM will predict the labels based on the reduced number of attributes.

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