ETRI Lab Seminar Findings for Intelligent Information Processing
Explore the insightful findings from ETRI Lab Seminar on various topics including F1 scores, AutoML classifiers, KoBERT, multi-label classification, and more. Dive into the world of cutting-edge research in information processing and machine learning.
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Presentation Transcript
ETRI Intelligent Information Processing Lab KiHoon Lee 1
Lab Seminar ETRI ETRI 2
Lab Seminar ETRI ETRI F1-Score : 70~80% 3
Lab Seminar ETRI ETRI 13,462 8:2 Train Test neutral 9291 2249 happy 1014 180 surprise 129 27 angry 124 23 disgust 56 5 sad 116 5 fear 39 4 4
Lab Seminar ETRI AutoML Logistic Regression Decision Tree Classifier SVM Ridge Classifier Random Forest Classifier Ada Boost Classifier Gradient Boosting Classifier Linear Discriminant Analysis Extra Trees Classifier XGboost lightgbm CatBoost Classifier Dummy Classifier neutral K-Fold > F1 Score =0.7608 5
Lab Seminar ETRI KoBERT, KoRoBERTa F1-Score : 0.953 Accuracy : 0.909 6
Lab Seminar ETRI 1. SMOTE ( k-NN ) 2. , 100 > + , , 250% 3. Neutral 50% 4. > Dataset 7
Lab Seminar ETRI 1. ( Dialog) 2. 3. (0.8, 0.2, 0.0, 0.0 ...) 4. UAT 5. ( (1D CNN) + (Mels) + (NLP)) > > 1 8
Lab Seminar ETRI 1. 2. > 3. Mulit Label Classification F1-Score : 0.8327 Accuracy : 0.8065 Angry Disgust Fear Happy Neutral Sad Suprise 35 16 8 254 2019 32 49 9
