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On cascading small decision trees

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Julià Minguillón
Julià Minguillón
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On Cascading Small Decision Trees Julià Minguillón Combinatorics and Digital Communications Group (CCD) Autonomous University of Barcelona (UAB) Barcelona, Spain http://www.tesisenxarxa.net/TESIS_UAB/AVAILABLE/TDX-1209102-150635/jma1de1.pdf
Table of contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Decision trees ,[object Object],[object Object],[object Object],[object Object]
Why decision trees? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Growing decision trees (binary) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Growing algorithm parameters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Splitting criterion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Labelling rule ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Progressive decision trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Growing progressive decision trees ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example (I) M 1 M 0 M 0 1 M
Example (II) M 0 1 M 1 0 0 1 M M M
Example (III) 1 0 M 0 1
Combining classifiers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cascading generalization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Type A progressive decision trees ,[object Object],T D Y D’
Type B progressive decision trees ,[object Object],T D Y D’
Type C progressive decision trees ,[object Object],T D Y D’
Experimental results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],} real projects
Document layout recognition (I) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Document layout recognition (II) ,[object Object],0.078 38 8.56 721 211200 / 211200 8 x 8 Error d max R |T| Num. Blocks Size
Document layout recognition (III) ,[object Object],0.042 6 3.72 11 21052 / 53760 16 x 16 0.047 6 4.17 14 7856 / 13440 32 x 32 0.089 4 2.77 6 3360 / 3360 64 x 64 0.065 8 4.73 18 27892 / 215040 8 x 8 Error d max R |T| Num. Blocks Size
Hyperspectral imaging (I) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Hyperspectral imaging (II) ,[object Object],[object Object],0.163 1.0 9.83 836 T 1 Error P T R |T| Tree 0.092 0.722 9.60 650 T 2 Error P T R |T| Tree
Hyperspectral imaging (III) ,[object Object],0.199 0.383 2.14 8 T 3B 0.056 0.523 3.02 9 T 3A 0.094 0.706 4.84 44 T 3 Error P T R |T| Tree
Brain tumour classification (I) ,[object Object],[object Object],[object Object],[object Object]
Brain tumour classification (II) ,[object Object],k -NN LDA DT X V Y ,[object Object],[object Object]
Brain tumour classification (III) Normal 100% Tumour 99.5% Benign 92.1% Malignant 94.9% Grade II 82.6% Grade IV 94.7% 98.9% Grade III 0% Astro 94.1% Oligo 100% 84.0% 89.9% 83.8% Secondary 91.4% Primary 81.8% 75.0% MN+SCH+HB ASTII+OD GLB+LYM+PNET+MET
UCI collection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experiments setup ,[object Object],[object Object],[object Object],[object Object]
Bias-variance decomposition ,[object Object],[object Object],[object Object],[object Object]
Empirical evaluation summary (I) ,[object Object],[object Object],[object Object]
Empirical evaluation summary (II) ,[object Object],[object Object],[object Object]
Theoretical issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Error generalization bounds ,[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Further research ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Selected references ,[object Object],[object Object],[object Object],[object Object]

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On cascading small decision trees

  • 1. On Cascading Small Decision Trees Julià Minguillón Combinatorics and Digital Communications Group (CCD) Autonomous University of Barcelona (UAB) Barcelona, Spain http://www.tesisenxarxa.net/TESIS_UAB/AVAILABLE/TDX-1209102-150635/jma1de1.pdf
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  • 13. Example (I) M 1 M 0 M 0 1 M
  • 14. Example (II) M 0 1 M 1 0 0 1 M M M
  • 15. Example (III) 1 0 M 0 1
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  • 30. Brain tumour classification (III) Normal 100% Tumour 99.5% Benign 92.1% Malignant 94.9% Grade II 82.6% Grade IV 94.7% 98.9% Grade III 0% Astro 94.1% Oligo 100% 84.0% 89.9% 83.8% Secondary 91.4% Primary 81.8% 75.0% MN+SCH+HB ASTII+OD GLB+LYM+PNET+MET
  • 31.
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