Project 1 Announcement March 22th, 2016

Transcription

1 Project 1 Announcement March 22th, Artificial Intelligence Course of 2016 Spring Instructor: Byoung-Tak Zhang Teaching Assistant: Seong-ho Son / Hyo-sun Chun Department of Computer Science and Engineering Seoul National University

2 Project 1 Bayesian Network experiment using Weka Reports to be submitted Progress report (1 ~ 2 pages) Submission due: April 21st, Thursday Optional: No delay penalties Final report (5 pages) Submission due: May 12 th, Thursday Delay penalty: -1 point / day How to submit shson@bi.snu.ac.kr File name: AI16s_prj1_YOURNAME ex) AI16s_prj1_SEONGHOSON.pdf You can also submit the report at classes.

3 Project 1 Things to be done Compare the graph structures and performances between experiments, while altering parameter settings and algorithms Examine the characteristics of different algorithms Progress report No fixed format: Include data, experiment plan, description of algorithm, etc. Final report Graphs of experiment results and analyses are required Grading policy (15 points in total) Comparison of experiment results Comparison between different parameters: Naïve bayes + simple estimator (7 points) Comparison between different searching algorithms (8 points) Progress report (2 bonus points)

4 Choose any data from below Breast Cancer 1 (9 attributes: age, menopause, tumor-size,...) Breast Cancer 2 (16 attributes: density, location, age, mass, size, shape,...) Non-Hodgkin Lymphoma (9 attributes: age, general health status, clinical stage, surgery,...) Census-income (14 attributes: age, work class, education, occupation, ) Car (6 attributes : buying, maint, doors, ) Adult (14 attributes : age, workclass, fnlwgt, ) Iris (4 attributes : sepal_length, sepal_width, ) If you want, you can use other data. (Please report TA if you are choosing data other than ones given, or have problem converting them into.arff format.)

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6 Run Explorer

7 Open file

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9 (Optional) Make new datasets:.data to.arff 1) Visit UCI ML Repository (

10 (Optional) Make new datasets:.data to.arff 2) Click View ALL Data Sets 3) Choose among datasets which are for Classification with Categorical attributes

11 (Optional) Make new datasets:.data to.arff 4) Click on Data Folder 5) Download files which end with.data

12 (Optional) Make new datasets:.data to.arff 6) Go back to the previous page, check Attribute Information

13 (Optional) Make new datasets:.data to.arff 7) Open the.data file (use notepad) 8) Copy the attribute names of the dataset in the first line of.data file (End the first line with ENTER key!) 9) Save the file with extension.csv

14 (Optional) Make new datasets:.data to.arff 10) Enter Explorer of Weka, click Open file 11) Change the file type to *.csv, load the file created at 9)

15 (Optional) Make new datasets:.data to.arff Error?) If you encounter an error like below, then add an attribute class in the first line of.csv file we created at 9). (If you still have errors, contact TA.)

16 (Optional) Make new datasets:.data to.arff 12) Once the.csv file opens, click on Save and save the file as.arff file. A new dataset is ready to be used!

17 Go Classify tab

18 Choose classifier

19 Use BayesNet

20 Click here & change parameter

21 Change probability estimation algorithm (Not recommended) It is recommended not to change estimator (Ones other than SimpleEstimator usually result in errors.) If you still want to try other estimators, set the searchalgorithm to Naivebayes. Then BMAEstimator and MultiNomialBMAEstimator will work on some datasets. (BayesNetEstimator does not work in most cases.)

22 Change alpha value of simple estimator Simple estimator == Laplace smoothing == simple counting

23 Change structure learning algorithm (select Naïve Bayes for experiments on parameters)

24 Click start

25 See results

26 (click right button) Visualize Graph

27 Appendix:

28 Appendix: Naïve Bayes Assuming the attributes are independent of each other, we have a Naïve Bayesian Network: P(play=yes)=9/14, with Laplace correction: P(play=yes)=9+1/14+2=0.625 In general, to make Laplace correction, we add an initial count (1) to the total of all instances with a given attribute value, and we add the number of distinct values of the same attribute to the total number of instances in the group.

29 Appendix: Naïve Bayes And to fill the Conditional Probability Tables we compute conditional probabilities for each node in form: Pr (attribute=value parents values) for each combinations of attributes values in parent nodes P(outlook=sunny play=yes) =(2+1)/(9+3)=3/12 P(outlook=rainy play=yes) =(3+1)/(9+3)=4/12 Sum is1 P(outlook=overcast play=ye s) =(4+1)/(9+3)=5/12 P(outlook=sunny play=yes) =(2+1)/(9+3)=3/12 P(outlook=sunny play=no) =(3+1)/(5+3)=4/8 Sum is NOT 1