Progress Seminar 2019.2.12 신희안.

Presentation on theme: "Progress Seminar 2019.2.12 신희안."— Presentation transcript:

1 Progress Seminar 신희안

2 연구 진행 상황 보고서 인공 심폐기 Suture Force HF Classification 기타 2주전 계획 연구 결과 문제점
Software validation(동아) 배터리 성능검사 KIMES 기기 전달 나일론 시편 제작 배송 완료 여러 알고리즘 적용 문제점 및 대책 Flowmeter 1대 고장 목표 및 계획 데이터 처리 논문 참고

3 ① ② Data Preprocessing …. …. X : input Y : death related parameter
Parameters …. Drop column: Missing>Threshold Patients Drop row: with missing value …. X : input Y : death related parameter MATLAB preprocessing  Pandas(python lib.) preprocessing

4 :output = follow up duration
Random Forest :output = follow up duration n_estimator: number of trees in forest max_depth:  max number of levels in each decision tree max_features: number of features considered when splitting node 80%: Train dataset 20%: Test dataset Each epoch: Randomly pick hyperparameter values for the model Fit train dataset to the model 5 fold cross validation of the model with test dataset Print out score for each selected hyperparameter model Can hold both categorical & numerical Normalization not needed Hyperparameter tuning 중요

5 Random Forest <Regression1> <Regression2>
X : input Y : Follow up duration (206 params.) Select parameters (29 params.) with Pearson correlation> 0.1 Follow up duration row X : input Y : Follow up duration R^2 score: Train data score = 0.926 Test data score = 0.481 hyperparameter 변화에도 여전히 5fold validation score 변화가 거의 없음 R^2 score: Train data score = 0.752 Test data score = 0.520

6 All params. Hi-corr. Params.
Random Forest Follow up duration  9 Categories (6 months * n) Regression  Classification <Classification> All params. Hi-corr. Params. Random Forest ~30% Xgboost ANN ~65% Score = Accuracy….? Xgboost : builds tree one at a time 유사한 accuracy…. Input data 변형 필요