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CS, NcState
CS, NcState
TecnologiaAperfeiçoamento pessoal
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Data Mining a Mushroom Dataset Raymond Borges Jarilyn Hernandez
Outline  Background  Introduction  Hypotheses  Methodology  Results  Conclusions  Future Work
Background Previous Work
The Mushroom Dataset  Hypothetical examples of 23 species from Agaricus and Lepiota families  Class attribute: Edibility Edible(4,208)51.8% Poisonous(3,916)48.2% Data Set Number of Multivariate 8124 Area: Life Characteristics: Instances: Attribute Number of Date Categorical 22 1987 Characteristics: Attributes: Donated:
Benchmark ruleset 1. Odor = not almond or anise or none (120 poisonous cases missed, 98.52% accuracy) 2. Spore-print-color =green (48 cases missed, 99.41% accuracy) 3. Odor=none and stalk-surface-below-ring = scaly and stalk-color-above-ring= not brown (8 cases missed, 99.90% accuracy) 4. Habitat= leaves and cap-color=white or 4. Population=clustered and cap-color=white (100% accuracy)
The Mushroom Dataset 22 Attributes 18 Visually on Mushroom 4 Others 1 Habitat 1 Population 1 Bruises 1 Odor
Visual Attribute ruleset Only 4 attrib.(100% accuracy) 1. Stalk surface above ring = not silky and ring number = not one, (79% accuracy JRIP) 2. Population not clustered(80% accuracy J48) Once retrieved test these two rules: 3. Odor = not bad, (98% accuracy J48) 4. Spore print color = not green, (100% J48)
Results  Odor and spore color may be the best attributes statistically but in the field  Focused on visual-queue attributes, e.g. habitat, population, cap and stalk  Obtained a more practical classification
Introduction Project III
Introduction Taking into account human Based on:  Lightingconditions  Mushroom stage in lifecycle  Humidity  Seasons  Human senses?  other unknown factors…
Introduction Some attributes difficult to discern Textures, Shapes or Colors like:  Brown  Chocolate  Buff  Cinnamon
Hypotheses 1. Complex attributes = Higher error probability 2. Human senses + external factors = Big impact So… Ruleset will change to approach reality Some attributes will fair much better than others
Methodology
Methodology Collect survey responses: 1. Evaluate species in different conditions 2. Measure overall accuracy 3. Weight attributes based on survey performance
Methodology part 1 Take 3 mushroom species  Agaricus Abruptibulbus  Agaricus Augustus  Lepiota Rubrotincta Place under 2 distinct set of conditions
Methodology part 2 5 questions per species in each condition Augustus Rubrotincta Abruptibulbus Augustus Rubrotincta Abruptibulbus under conditions X under conditions X under conditions Y under conditions Y
Methodology part 3  Design Tutorial (SurveyMonkey.com)  Design Website (Weebly.com) Get people to take survey (hardest part)  Designed Flyers  Poster boards  Business cards
Survey at Mountainlair
Survey at Mountainlair
Methodology 4  Calculate survey test scores  Calculate species’ accuracy variation  Calculate attributes’ accuracy variation  Calculate attribute weights  Use data mining tools to find best ruleset
Weighting Methodology 
Results
Overall Survey Results  30 questions per survey  15 Attributes measured  37 completed surveys  1,110 answered questions  Overall A 0 Survey Grades B 1 C 7 D 8 F 14  Highest was 24 out of 30 correct answers
Results Survey Accuracy per Attribute 100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%
Attribute Accuracy Attribute Variation veil color 37.8 10.8 ring number 59.5 5.4 stalk shape 33.75 18.9 cap shape 32.45 48.7 cap surface 32.45 5.4 cap color 81.1 5.4 gill spacing 78.4 16.2 stalk root 45.95 21.7 stalk color above ring 59.45 64.9 stalk color below ring 67.6 10.8 gill size 36.45 13.5 stalk surface below ring 78.4 5.4 ring type 73 5.4 stalk surface above ring 63.55 2.7 gill color 63.55 13.5 0 10 20 30 40 50 60 70 80 90 100
Weighted Attributes 100 90 80 70 60 Weight 50 40 30 20 10 76.7 74.2 69 65.7 61.8 60.3 56.3 55 36 33.7 31.5 30.7 27.4 20.9 16.7 0
J48 Tree 99.6% E = Edible Classification P = Poisonous E P P E P P P P almond creosote foul anise musty none pungent spicy fishy E E E E P E E E black brown buff chocolate green orange purple white yellow E P E E silky scaly fibrous smooth
J48 Tree 99.9% E = Edible Classification P = Poisonous E P P E P P P P almond creosote foul anise musty none pungent spicy fishy E E E E P E E E black brown buff chocolate green orange purple white yellow E E E scaly fibrous silky smooth P P P P P P P E evanescent flaring zone sheathing none large cobwebby pendant
Attribute Accuracy 100 90 A 80 Cap Color, 10 c Stalk Surface Below, 4 Ring Type, 8 70 Stalk Color Below, 9 c Stalk Surface Above, 4 60 Ring Number, 3 Stalk Color Above, 9 u 50 Stalk Root, 7 r 40 Veil Color, 4 a 30 Stalk Shape, 2 Cap Surface, 4 Cap Shape, 6 c 20 y 10 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Complexity
Conclusions
Conclusion Complex attributes = Higher error probability Hypothesis 1: False They are actually more accurate the more complex the attribute Fat spheres = Complex attributes Height = Survey accuracy
Conclusion Human senses + external factors = Big impact Hypothesis 2: True  24% change in correctly identifying attributes due to ambient environment conditions  1.2 questions answered incorrectly out of 5 due to ambient environments of mushrooms
Future Work  Evaluatemushroom expertise for increase in mushroom attribute identification accuracy  Measure Spore print color and Odor in surveys?
Questions?

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Project 3 mushrooms

  • 1. Data Mining a Mushroom Dataset Raymond Borges Jarilyn Hernandez
  • 2. Outline  Background  Introduction  Hypotheses  Methodology  Results  Conclusions  Future Work
  • 4. The Mushroom Dataset  Hypothetical examples of 23 species from Agaricus and Lepiota families  Class attribute: Edibility Edible(4,208)51.8% Poisonous(3,916)48.2% Data Set Number of Multivariate 8124 Area: Life Characteristics: Instances: Attribute Number of Date Categorical 22 1987 Characteristics: Attributes: Donated:
  • 5. Benchmark ruleset 1. Odor = not almond or anise or none (120 poisonous cases missed, 98.52% accuracy) 2. Spore-print-color =green (48 cases missed, 99.41% accuracy) 3. Odor=none and stalk-surface-below-ring = scaly and stalk-color-above-ring= not brown (8 cases missed, 99.90% accuracy) 4. Habitat= leaves and cap-color=white or 4. Population=clustered and cap-color=white (100% accuracy)
  • 6. The Mushroom Dataset 22 Attributes 18 Visually on Mushroom 4 Others 1 Habitat 1 Population 1 Bruises 1 Odor
  • 7.
  • 8. Visual Attribute ruleset Only 4 attrib.(100% accuracy) 1. Stalk surface above ring = not silky and ring number = not one, (79% accuracy JRIP) 2. Population not clustered(80% accuracy J48) Once retrieved test these two rules: 3. Odor = not bad, (98% accuracy J48) 4. Spore print color = not green, (100% J48)
  • 9. Results  Odor and spore color may be the best attributes statistically but in the field  Focused on visual-queue attributes, e.g. habitat, population, cap and stalk  Obtained a more practical classification
  • 11. Introduction Taking into account human Based on:  Lightingconditions  Mushroom stage in lifecycle  Humidity  Seasons  Human senses?  other unknown factors…
  • 12. Introduction Some attributes difficult to discern Textures, Shapes or Colors like:  Brown  Chocolate  Buff  Cinnamon
  • 13. Hypotheses 1. Complex attributes = Higher error probability 2. Human senses + external factors = Big impact So… Ruleset will change to approach reality Some attributes will fair much better than others
  • 15. Methodology Collect survey responses: 1. Evaluate species in different conditions 2. Measure overall accuracy 3. Weight attributes based on survey performance
  • 16. Methodology part 1 Take 3 mushroom species  Agaricus Abruptibulbus  Agaricus Augustus  Lepiota Rubrotincta Place under 2 distinct set of conditions
  • 17. Methodology part 2 5 questions per species in each condition Augustus Rubrotincta Abruptibulbus Augustus Rubrotincta Abruptibulbus under conditions X under conditions X under conditions Y under conditions Y
  • 18. Methodology part 3  Design Tutorial (SurveyMonkey.com)  Design Website (Weebly.com) Get people to take survey (hardest part)  Designed Flyers  Poster boards  Business cards
  • 19.
  • 22. Methodology 4  Calculate survey test scores  Calculate species’ accuracy variation  Calculate attributes’ accuracy variation  Calculate attribute weights  Use data mining tools to find best ruleset
  • 25. Overall Survey Results  30 questions per survey  15 Attributes measured  37 completed surveys  1,110 answered questions  Overall A 0 Survey Grades B 1 C 7 D 8 F 14  Highest was 24 out of 30 correct answers
  • 26. Results Survey Accuracy per Attribute 100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%
  • 27. Attribute Accuracy Attribute Variation veil color 37.8 10.8 ring number 59.5 5.4 stalk shape 33.75 18.9 cap shape 32.45 48.7 cap surface 32.45 5.4 cap color 81.1 5.4 gill spacing 78.4 16.2 stalk root 45.95 21.7 stalk color above ring 59.45 64.9 stalk color below ring 67.6 10.8 gill size 36.45 13.5 stalk surface below ring 78.4 5.4 ring type 73 5.4 stalk surface above ring 63.55 2.7 gill color 63.55 13.5 0 10 20 30 40 50 60 70 80 90 100
  • 28. Weighted Attributes 100 90 80 70 60 Weight 50 40 30 20 10 76.7 74.2 69 65.7 61.8 60.3 56.3 55 36 33.7 31.5 30.7 27.4 20.9 16.7 0
  • 29. J48 Tree 99.6% E = Edible Classification P = Poisonous E P P E P P P P almond creosote foul anise musty none pungent spicy fishy E E E E P E E E black brown buff chocolate green orange purple white yellow E P E E silky scaly fibrous smooth
  • 30. J48 Tree 99.9% E = Edible Classification P = Poisonous E P P E P P P P almond creosote foul anise musty none pungent spicy fishy E E E E P E E E black brown buff chocolate green orange purple white yellow E E E scaly fibrous silky smooth P P P P P P P E evanescent flaring zone sheathing none large cobwebby pendant
  • 31. Attribute Accuracy 100 90 A 80 Cap Color, 10 c Stalk Surface Below, 4 Ring Type, 8 70 Stalk Color Below, 9 c Stalk Surface Above, 4 60 Ring Number, 3 Stalk Color Above, 9 u 50 Stalk Root, 7 r 40 Veil Color, 4 a 30 Stalk Shape, 2 Cap Surface, 4 Cap Shape, 6 c 20 y 10 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Complexity
  • 33. Conclusion Complex attributes = Higher error probability Hypothesis 1: False They are actually more accurate the more complex the attribute Fat spheres = Complex attributes Height = Survey accuracy
  • 34. Conclusion Human senses + external factors = Big impact Hypothesis 2: True  24% change in correctly identifying attributes due to ambient environment conditions  1.2 questions answered incorrectly out of 5 due to ambient environments of mushrooms
  • 35. Future Work  Evaluatemushroom expertise for increase in mushroom attribute identification accuracy  Measure Spore print color and Odor in surveys?