CSC570 Lecture 07

jgs CSC 570 Current Topics in Computer Science Applied Affective
Computing Lecture 07: Ensemble Methods Dr. Javier Gonzalez-Sanchez [email protected] www.javiergs.com Building 14 -227 Office Hours: By appointment

jgs Previously …

jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 |
3 Homework • Open or Closed Eyes VS. Brain • 5 diverse stimulation scenarios VS. Brain • 5 diverse stimulation scenarios VS. Affect Follow 2 approaches: a) Clustering as described today (EM, K-means, Density) b) Explore another solution to the best of your knowledge (Machine Learning, Data mining, Statistics) Due: Monday (April 24)

4 Thoughts? Clustering was easy, What about something more precise for our data (such as a RandomForest) ?

5 Machine Learning EM

jgs Classification Decision Tree

7 Data

8 Example

9 Example Which Attribute Should be the ROOT?

10 Entropy Entropy is defined as the measurement of degree of randomness or in other words, it is the increase in the disorganization within a system.

11 Gain Information gain is the reduction in entropy or surprise by transforming a dataset

12 Next

13 Solution

14 Weka | J48 CSV file

15 Evaluation (using 99% or 66% of training)

16 Dataset 2

17 Only 01 and 02

18 Only 01 and 02

19 All 14 EEG columns

jgs Ensemble Machine Learning Algorithms Random Forest

22 Definition § Ensemble algorithms combine the predictions from multiple models.

23 Random Forest § Random Forest is bagging for decision trees that can be used for classification or regression. § Decision trees are constructed using a greedy algorithm that selects the best split point at each step in the tree building process § Random Forest disrupts the greedy splitting algorithm during tree creation so that split points can only be selected from a random subset of the input attributes.

24 Random Forest

25 Notes § Random forest classifier is only to be used when the data set is huge. If data set is not very huge the accuracy by Random forest classifier is less than accuracy by a normal single tree.

26 How many Trees? § Default 10 § Suggest a number of trees between 64 - 128 trees.

27 Weka

28 Evaluation

29 Dataset 2

30 Only 01 and 02

32 Lab 1 RandomForest for our CSV dataset with 5 categories (we have 20 files to combine in one dataset)

jgs Eyes

34 How it Works https://connect.tobii.com/s/article/How-do-Tobii-eye-trackers-work

35 Eye

36 Eye Timestamp GPX GPY Pupil Left Validity L Pupil Right Validity R Fixation Event AOI 101124162405582 636 199 2.759313 0 2.88406 0 48 Content 101124162405599 641 207 2.684893 0 2.855817 0 48 Content 101124162405615 659 211 2.624458 0 2.903861 0 48 Content 101124162405632 644 201 2.636186 0 2.916132 0 48 Content 101124162405649 644 213 2.690685 0 2.831013 0 48 Content 101124162405666 628 194 2.651784 0 2.869714 0 48 Content 101124162405682 614 177 2.829281 0 2.899828 0 48 Content 101124162405699 701 249 2.780344 0 2.907665 0 49 Content 101124162405716 906 341 2.853761 0 2.916398 0 49 Content 101124162405732 947 398 2.829427 0 2.889944 0 49 Content 101124162405749 941 400 2.826602 0 2.881179 0 49 Content 101124162405766 938 403 2.78699 0 2.87948 0 49 KeyPress Content 101124162405782 937 411 2.803387 0 2.821803 0 49 Content 101124162405799 934 397 2.819166 0 2.871547 0 49 Content 101124162405816 941 407 2.811687 0 2.817927 0 49 Content 101124162405832 946 405 2.857419 0 2.857427 0 49 Content 101124162405849 0 0 -1 4 -1 4 49 Content

37 Eye 30 o 60 frames per second 30 o 60 inferences per second 1,800 o 3,600 values per minute 108,000 o 216, 000 values per hour

38 Eye

39 Affect Recognition BCI and Gaze Points engagement

40 Affect Recognition BCI and Gaze Points frustration

41 Affect Recognition BCI and Gaze Points engagement

42 Affect Recognition BCI and Gaze Points frustration

jgs Gaze Tracking Low Cost

44 Do you know JavaScript?

45 § https://webgazer.cs.brown.edu § eye tracking library using common webcams to infer the eye-gaze locations of web visitors on a page in real-time. § written in JavaScript § can be integrated into a website § runs entirely in the client browser, so no video data needs to be sent to a server, and it requires the user's consent to access their webcam. WebGazer

46 § https://github.com/brownhci/WebGazer/blob/master/www/calibration.html Callibration

47 § asynchronous data store with a simple API § allows developers to store many types of data instead of just strings. localforage

48 <html> <head> <script src="./webgazer.js"></script> </head> <body> <div style="text-align: right;" id="gazeData"></div> <script> webgazer.resume(); webgazer.setGazeListener(function(data, elapsedTime) { if (data != null) { var x = data.x; var y = data.y; document.getElementById("gazeData").innerHTML = "Gaze coordinates: x=" + x + ", y=" + y; } }).begin(); </script>  Template

49  <style> html, body {height: 100%;margin: 0; padding: 0; } table {width: 100%;height: 100%;border-collapse: collapse;} td {border: 1px solid black;} </style> <table> <tr> <td>Cell 1</td> <td>Cell 2</td> <td>Cell 3</td> </tr> <tr> <td>Cell 4</td> <td>Cell 5</td> <td>Cell 6</td> </tr> <tr> <td>Cell 7</td> <td>Cell 8</td> <td>Cell 9</td> </tr> </table> </body> </html> Template

50 Example

51 Thoughts? For a Low-Cost, Low-Resolution approach, Could it be possible to After a while To cluster? What could be the result of doing that? Which approach could work better (K-mean, DBSCAN, EM)?

52 Thoughts? How to connect this info with EEG? timestamp in JS? Save local data in JS?

53 Questions

54 Office Hours Tuesday and Thursday 3 - 5 pm But an appointment required Sent me an email – [email protected]

jgs CSC 570 Applied Affective Computing Javier Gonzalez-Sanchez, Ph.D. [email protected]
Spring 2023 Copyright. These slides can only be used as study material for the class CSC308 at Cal Poly. They cannot be distributed or used for another purpose.

CSC570 Lecture 07

CSC570 Lecture 07

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