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Some Idea on Subgroup Discovery

basisbasis Member Posts: 5 Contributor II
edited November 2018 in Help
Hello,

I am a newbie so I hope you could tolerate me..  :D

I have a high dimensional data that I would like to apply a feature selection process..

The data is from bioinformatics applications. More specifically I am trying to analyze SNP-Complex Disease association but the problem is there are like hundreds of thousands of snps. I would like to decrease this number via feature selection.

I come up with rapidminer and I think it is a great tool but I cannot fully use its potential since I am a nebie.

Now I was playing with the GUI and I saw SubgroupDiscovery operator under Learner>Supervised>Meta.. And I am also using templates from process wizard (version 4.4 by the way).. I am choosing feature selection and by default it uses libsvm. I am replacing the libsvm with SubgroupDiscovery and trying to run but I got the error message saying:

SubgroupDiscovery: SubgroupDiscovery: Operator has 0 children, should be 1

well it seems self explanatory but as I said I am a newbie on this.  :'(

So my questions are:

1. what is the problem with subgroupdiscovery?
2. can anybody suggest good operators for feature selection for high dimensional data?

my data is like (just a part of it):

ID Gender CaseControl 1A1B11 1A1B13 1A1B31 1A1B33 2A2B11 2A2B13 2A2B31 2A2B33 3A3B11 3A3B14 3A3B41 3A3B44 4A4B22 4A4B24 4A4B42 4A4B44 5A5B11 5A5B12 5A5B21 5A5B22 6A6B11 6A6B13 6A6B31 6A6B33 7A7B22 7A7B24 7A7B42 7A7B44 8A8B22 8A8B23 8A8B32 8A8B33 9A9B33 9A9B34 9A9B43 9A9B44 10A10B22 10A10B24 10A10B42 10A10B44 11A11B22 11A11B24 11A11B42 11A11B44 12A12B11 12A12B12 12A12B21 12A12B22 13A13B11 13A13B12 13A13B21 13A13B22 14A14B11 14A14B13 14A14B31 14A14B33 15A15B22 15A15B24 15A15B42 15A15B44 16A16B22 16A16B23 16A16B32 16A16B33 17A17B11 17A17B13 17A17B31 17A17B33 18A18B22 18A18B23 18A18B32 18A18B33 19A19B11 19A19B14 19A19B41 19A19B44 20A20B33 20A20B34 20A20B43 21A21B11 21A21B13 21A21B31 21A21B33 22A22B22 22A22B24 22A22B42 22A22B44 23A23B22 23A23B23 23A23B32 23A23B33 24A24B11 24A24B13 24A24B31 24A24B33 25A25B22 25A25B24 25A25B42 25A25B44 26A26B33 26A26B34 26A26B43 26A26B44 27A27B22 27A27B23 27A27B32 27A27B33 28A28B22 28A28B23 28A28B32 28A28B33 29A29B22 29A29B24 29A29B42 29A29B44 30A30B11 30A30B12 30A30B21 30A30B22 31A31B22 31A31B24 31A31B42 31A31B44 32A32B11 32A32B13 32A32B31 32A32B33 33A33B11 33A33B13 33A33B31 33A33B33 34A34B11 34A34B12 34A34B21 34A34B22 35A35B22 35A35B24 35A35B42 35A35B44 36A36B22 36A36B23 36A36B32 36A36B33 37A37B12 37A37B21 37A37B22 38A38B11 38A38B13 38A38B31 38A38B33 39A39B22 39A39B23 39A39B32 39A39B33 40A40B22 40A40B24 40A40B42 40A40B44 41A41B11 41A41B12 41A41B21 41A41B22 42A42B22 42A42B24 42A42B42 42A42B44 43A43B11 43A43B13 43A43B31 43A43B33 44A44B11 44A44B12 44A44B21 44A44B22 45A45B22 45A45B24 45A45B42 45A45B44 46A46B22 46A46B24 46A46B42 46A46B44 47A47B12 47A47B21 47A47B22 48A48B22 48A48B24 48A48B42 48A48B44 49A49B22 49A49B24 49A49B42 49A49B44 50A50B11 50A50B13 50A50B31 50A50B33 51A51B22 51A51B24 51A51B42 51A51B44 52A52B22 52A52B24 52A52B42 52A52B44 53A53B22 53A53B24 53A53B42 53A53B44 54A54B11 54A54B12 54A54B21 54A54B22 55A55B22 55A55B24 55A55B42 55A55B44 56A56B22 56A56B23 56A56B32 56A56B33 57A57B11 57A57B13 57A57B31 57A57B33 58A58B33 58A58B34 58A58B43 58A58B44 59A59B13 59A59B31 59A59B33 60A60B22 60A60B23 60A60B32 60A60B33 61A61B11 61A61B14 61A61B41 61A61B44 62A62B22 62A62B23 62A62B32 62A62B33 63A63B33 63A63B34 63A63B43 63A63B44 64A64B11 64A64B13 64A64B31 64A64B33 65A65B11 65A65B13 65A65B31 65A65B33 66A66B22 66A66B24 66A66B42 66A66B44 67A67B11 67A67B13 67A67B31 67A67B33 68A68B22 68A68B24 68A68B42 69A69B33 69A69B34 69A69B43 70A70B14 70A70B41 70A70B44 71A71B11 71A71B13 71A71B31 71A71B33 72A72B22 72A72B24 72A72B42 72A72B44 73A73B11 73A73B14 73A73B41 73A73B44 74A74B33 74A74B34 74A74B43 74A74B44 75A75B22 75A75B24 75A75B42 75A75B44 76A76B11 76A76B14 76A76B41 76A76B44 77A77B22 77A77B23 77A77B32 77A77B33 78A78B22 78A78B24 78A78B42 78A78B44 79A79B11 79A79B13 79A79B31 79A79B33 80A80B11 80A80B14 80A80B41 80A80B44 81A81B24 81A81B42 81A81B44 82A82B34 82A82B43 82A82B44 83A83B11 83A83B13 83A83B31 83A83B33 84A84B11 84A84B13 84A84B31 84A84B33 85A85B22 85A85B23 85A85B32 85A85B33 86A86B11 86A86B13 86A86B31 86A86B33 87A87B22 87A87B24 87A87B42 87A87B44 88A88B11 88A88B13 88A88B31 88A88B33 89A89B11 89A89B13 89A89B31 89A89B33 90A90B22 90A90B24 90A90B42 90A90B44 91A91B11 91A91B13 91A91B31 91A91B33 92A92B33 92A92B34 92A92B43 92A92B44 93A93B33 93A93B34 93A93B43 93A93B44 94A94B22 94A94B24 94A94B42 94A94B44 95A95B22 95A95B23 95A95B32 95A95B33 96A96B22 96A96B24 96A96B42 96A96B44 97A97B11 97A97B13 97A97B31 97A97B33 98A98B11 98A98B13 98A98B31 98A98B33 99A99B22 99A99B24 99A99B42 99A99B44 100A100B11 100A100B13 100A100B31 100A100B33 101A101B33 101A101B34 101A101B43 101A101B44 102A102B11 102A102B12 102A102B21 102A102B22 103A103B11 103A103B13 103A103B31 103A103B33
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Answers

  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531   Unicorn
    Hi,
    the error only tries to say, that you need to insert another operator into the SubgroupDiscovery operator. This is then used by the SubgroupDiscovery. Since it is a meta Learner, you probably have to insert a learning algorithm.

    But I doubt that subgroup discovery is the best suitable algorithm for your problem. Usually you will have a label, indicating if the disease occures. If you want to select best features to predict this, you should use a SVM, since it copes well with high dimensions.

    SubgroupDiscovery tries to discover groups within the examples and not within the attributes...

    Greetings,
      Sebastian
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