A simple database containing 17 Boolean-valued attributes. The "type" attribute appears to be the class attribute. Here is a breakdown of which animals are in which type: (I find it unusual that there are 2 instances of "frog" and one of "girl"!) Class# -- Set of animals: ====== ==================================================== 1 -- (41) aardvark, antelope, bear, boar, buffalo, calf, cavy, cheetah, deer, dolphin, elephant, fruitbat, giraffe, girl, goat, gorilla, hamster, hare, leopard, lion, lynx, mink, mole, mongoose, opossum, oryx, platypus, polecat, pony, porpoise, puma, pussycat, raccoon, reindeer, seal, sealion, squirrel, vampire, vole, wallaby,wolf 2 -- (20) chicken, crow, dove, duck, flamingo, gull, hawk, kiwi, lark, ostrich, parakeet, penguin, pheasant, rhea, skimmer, skua, sparrow, swan, vulture, wren 3 -- (5) pitviper, seasnake, slowworm, tortoise, tuatara 4 -- (13) bass, carp, catfish, chub, dogfish, haddock, herring, pike, piranha, seahorse, sole, stingray, tuna 5 -- (4) frog, frog, newt, toad 6 -- (8) flea, gnat, honeybee, housefly, ladybird, moth, termite, wasp 7 -- (10) clam, crab, crayfish, lobster, octopus, scorpion, seawasp, slug, starfish, worm

1. Wellcome To Our presentation

2. Data Analysis with Weka zoo.arff

3. Dataset
◉Zoo.arff:
A simple database containing 17 Boolean-valued attributes. The "type“ attribute appears to be the
class attribute. Here is a breakdown of which animals are in which type.

4. Dataset Visualization

5. Pre-Processing
Why?
◉Incomplete Data
◉ Noisy Data
◉Inconsistent Data
How?
◉ Discretize
◉ Remove Duplicates
◉ RemoveUseless
◉ Etc
Case Study:
◉ It has duplicate (frog)
◉One attribute is misleading
(Animal name)

6. Classification
Decision Trees
 J48
Bayes
 Naive Bayes
Rule Based
 JRip

7. Classifiers Options Explained-Applied on Dataset
◉Training Data:
We use the whole
dataset as training set.
It gives the best
results for the data set
itself but does not
guarantee the best
test for unseen data.
◉Cross-Validation:
Divide the Data Set
into K subsamples and
use k-1 subsamples as
training data and one
subsample as test
data.
◉Percentage Split:
We divide the dataset
into two parts: the
first X% of the data
set is used as training
and the rest is used as
the test set.

8. JRIP Classifier Algorithm
Works fine for:
◉ Class: Missing classes values,
binary and Nominal Classes
◉ Attributes: Nominal Dates, Missing
Values, Numeric and Binary
Advantages
◉ As highly expressive as decision trees
◉Easy to interpret
◉ Easy to generate

9. Classifier Evaluation-Default Options JRip
Pre-Processed?-> NO Results

10. Classifier Evaluation->Cross Validation Increased JRip

11. Classifier Evaluation->Cross Validation JRip
Pre-Processed?-> NO Results: NO BIG CHANGE
Cross
Validation
Correctly
Classified Inst.
10 87.12
20 85.14
30 86.13
40 84.15
50 87.12
60 82.17
70 85.14

12. ClassifierClassifier Evaluation->Training Set JRip
Pre-Processed?-> NO Results

13. Pre-Processing- Why?
 Pre-Processing->
◉ RemoveDuplicates (instances),
◉ RemoveUseless (attributes)

14. RemoveDuplicates (instances), RemoveUseless (attributes)
◉RemoveDuplicates:
 Removes all duplicate instances
from the first batch of data it
receive
◉RemoveUseless:
 This filter removes attributes that
do not vary at all or that vary too
much

15. Classifier Evaluation->Default Options JRip
Pre-Processed?-> Yes Results

16. J48 Classifier Algorithm
Works fine for:
◉ Class: Missing classes values,
binary and Nominal Classes
◉ Attributes: Nominal Dates,
Missing Values, Numeric and
Binary
Advantages
◉ Easy to implement
◉Can use both categorical and
Continuous values
◉ Deals with noise

17. Default options J48

18. Default Options J48

19. Changing cross validation =20 J48

20. Changing cross validation value J48
Cross
Validation
Correctly
Classified Inst.
10 92.079
20 92.079
30 92.079
40 92.079
50 92.079
60 92.079
70 92.079

21. Any questions ?
Thanks!