Event log outcome predicition with CNN
20.09.02
- Try to use 'Traffic fine' event log instead of BPIC 2011 to cross check 'CNN VS LSTM' paper.
- In terms of accuracy, random forest doens't show much difference between CNN and LSTM performance in paper
- Change the research point to include event-based attributes in input as well as only activity and time related artifical attributes
Problem
- CNN requires same number of attributes in each events to fix kernel size and stride.
- Some event-based attributes relied on specific activity cause size imbalance in intra-case level and inter-case level.
Expected Solution
- By excluding problematic attributes, equalize event window size. ex) Only activity and time related attrs.
- Find alternative way to impute Nan value, i.e., how to handle Nan value in deep learning model?
- This research shall be not point to performance imporvement with CNN1d.
- But to develop encoding method in pre-processing stage that includes event-based and case-based attributes
20.09.01
- CNN1d also shows no further improvement in performance.
- Lets try to change dataset and use longer prefix length
20.08.31
- Try to change model of FC and CNN1d to be simpler than previous one.
- Even with simplifed model, validation loss didn't decrease even with decreasing training loss in CNN1d model.
- On the other hand, FC model, validation loss value is reduced in early stage while validation accuracy stays after around epoch 20.
20.08.27
- Fully connected network (FC) and Convolutional 1d network (CNN1d)with index base encoding done
- This time only consider event attributes including continuous and categorical ones.
- Compare RF, FC, CNN1d in binary label classification performance
- There are not that significant difference among those.
- FC and CNN1d overfitting are found. Validation loss doesn't decrease
- Need to think parameter tunning or redesign network could imporve result.
20.08.24
- Index base encoding pre-processing file uploaded
- BPIC 2011 with prefix 5 will be the test case for experiment
- Continuous columns are standardized to prevent bias
20.08.06
- Changing output layers but it was not problem.
- Data imbalance was not the problem.
- Let's consider attach embedding layer, since almost all input data is one hot vector.
- It worked!. In case of last state encoding, after equalizing input size, i.e., removing columns doesn't exist across all cases.
- After embedding layer before linear layers is efficient that just one hot vector
20.07.29
- Idea with combine CNN layer and random forest works well as in figure
- Following question is due to classification is too eay, more complicate situation is required to test
- Also still not sure CNN works as intended, extracting right features for binary classification.
- To check this question with CNN from Rule A, redo exepriment in same setting from different label classification by Rule B.
- Expected : If it works well, CNN doesnt' work well. If performance is bad, CNN validates it only compress and extract feature for Rule A.
20.07.28
- BPIC 2015_2 binary classification with CNN and Only MLP don't work well compare to RF.
20.07.27
- Preprocessing method, last state encoding, done
- Convert event log already sliced by prefix length to last state encoding with timestamp, continuous attributes, categorical attributes, and outcome labels
- Customdata loader is written