Problems Predicting Unseen Cases Of Nested Tables Data Mining Models

Lets take the following example:



Movie train table:
ID Class
1 +
2 +
3 -
4 +
5 -



Actor train nested table:
ID MovieID Gender
1 1 F
2 1 M
3 1 F
4 1 F
5 2 M
6 2 M
7 2 F
8 3 F
9 3 F
10 4 M
11 4 M
12 4 F
13 4 F
14 5 F
15 5 M



We want to build a classifier model in order to predict the Class of a Movie based on the Gender of movie's actors. To deal with the nested table Analysis Services maps each record of the nested table to an attribute of the case table. These attributes are named Actor(n).Gender with n = 1..15, and so they are dependent on the nested table record numbers. Both Microsoft Decision Trees and Microsoft Naive Bayes algorihms use these attributes without any modification.



We are implementing a Relational Naive Bayes algorithm and we are planning to aggregate such attributes in order to make them independent of the nested table record numbers.


Next step we tried to predict some unseen cases and here we face with
a very huge problem.


Lets take more two tables of unseen cases:



Movie test table:
ID Class
6 +
7 NULL
8 NULL



Actor test nested table:
ID MovieID Gender
1 6 F
2 6 M
3 6 F
4 6 F
16 7 F
17 7 M
18 7 F
19 7 F
20 7 F
21 8 M
22 8 M
23 8 F



Predicting the movie 6 Class is not a problem since the movie actors were included in the training dataset and when the records are mapped to attributes because they already exist in the model. But when you
try to predict movies (7 an 8) with unseen actors all new attributes are simply ignored in the ALGORITHM:redict call (in_ulCaseValues is zero!) because they do not exist in the model!



What is the solution?