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refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

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에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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---
id: wiki-2026-0508-imbalanced-data-handling
title: Imbalanced Data Handling
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [imbalanced data, class imbalance, SMOTE, oversampling, undersampling, class weight]
duplicate_of: none
source_trust_level: A
confidence_score: 0.95
verification_status: applied
tags: [machine-learning, imbalanced, smote, oversampling, class-weight, fraud]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
language: Python
framework: imbalanced-learn / scikit-learn
---
# Imbalanced Data Handling
## 매 한 줄
> **"매 class distribution 의 의 의 imbalance — 매 majority dominate"**. 매 fraud, 매 medical rare disease, 매 anomaly 의 common. 매 method: 매 oversample (SMOTE), undersample, class weight, focal loss, threshold tune. 매 evaluation: 매 accuracy 의 useless — PR-AUC, F1, MCC.
## 매 핵심
### 매 method
- **Resampling**:
- **Random oversample** (minority).
- **SMOTE**: 매 synthetic minority.
- **ADASYN**: adaptive.
- **Random undersample** (majority).
- **Tomek links**: 매 boundary clean.
- **Class weight**.
- **Loss-based**: focal loss, weighted CE.
- **Threshold tuning**: 매 default 0.5 의 X.
- **Anomaly detection** (1-class).
- **Cost-sensitive learning**.
### 매 metric
- **PR-AUC** (Average Precision).
- **F1** / Macro-F1.
- **MCC** (Matthews Correlation Coefficient).
- **Cohen's κ**.
- **Confusion matrix**.
### 매 응용
1. **Fraud detection**.
2. **Medical** (rare disease).
3. **Anomaly detection**.
4. **Customer churn**.
5. **Click prediction**.
## 💻 패턴
### Class weight (sklearn)
```python
from sklearn.utils import class_weight
weights = class_weight.compute_class_weight('balanced', classes=np.unique(y), y=y)
weight_dict = dict(zip(np.unique(y), weights))
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(class_weight=weight_dict).fit(X, y)
```
### SMOTE (imbalanced-learn)
```python
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=0)
X_res, y_res = sm.fit_resample(X, y)
```
### SMOTE-NC (mixed numerical + categorical)
```python
from imblearn.over_sampling import SMOTENC
smnc = SMOTENC(categorical_features=[0, 2, 5], random_state=0)
X_res, y_res = smnc.fit_resample(X, y)
```
### Random undersample
```python
from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler(sampling_strategy=0.5, random_state=0)
X_res, y_res = rus.fit_resample(X, y)
```
### Combined (SMOTE + Tomek)
```python
from imblearn.combine import SMOTETomek
smt = SMOTETomek(random_state=0)
X_res, y_res = smt.fit_resample(X, y)
```
### Pipeline (avoid leakage)
```python
from imblearn.pipeline import Pipeline as ImbPipeline
pipe = ImbPipeline([
('scaler', StandardScaler()),
('smote', SMOTE()),
('clf', LogisticRegression()),
])
# 매 SMOTE applied 매 매 fold (CV-safe)
from sklearn.model_selection import cross_val_score
scores = cross_val_score(pipe, X, y, cv=5, scoring='f1')
```
### Focal loss (PyTorch)
```python
import torch.nn.functional as F
def focal_loss(logits, targets, alpha=0.25, gamma=2.0):
p = torch.sigmoid(logits)
p_t = p * targets + (1 - p) * (1 - targets)
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
bce = F.binary_cross_entropy_with_logits(logits, targets, reduction='none')
return (alpha_t * (1 - p_t) ** gamma * bce).mean()
```
### Threshold tuning
```python
from sklearn.metrics import precision_recall_curve
y_score = model.predict_proba(X_val)[:, 1]
prec, rec, thr = precision_recall_curve(y_val, y_score)
f1_scores = 2 * prec * rec / (prec + rec + 1e-9)
best_thr = thr[f1_scores.argmax()]
y_pred = (y_score > best_thr).astype(int)
```
### XGBoost scale_pos_weight
```python
import xgboost as xgb
ratio = sum(y == 0) / sum(y == 1)
model = xgb.XGBClassifier(scale_pos_weight=ratio).fit(X, y)
```
### Eval metrics (proper)
```python
from sklearn.metrics import classification_report, average_precision_score, matthews_corrcoef, confusion_matrix
print(classification_report(y_val, y_pred))
print(f'PR-AUC: {average_precision_score(y_val, y_score):.3f}')
print(f'MCC: {matthews_corrcoef(y_val, y_pred):.3f}')
print(confusion_matrix(y_val, y_pred))
```
### Cost-sensitive learning
```python
COST_MATRIX = np.array([[0, 1], [10, 0]]) # 매 FN cost = 10x FP
def cost_sensitive_predict(probs, cost):
expected_cost = probs @ cost
return expected_cost.argmin(axis=1)
```
### One-class anomaly
```python
from sklearn.ensemble import IsolationForest
iso = IsolationForest(contamination=0.01).fit(X_majority)
anomalies = iso.predict(X_test) == -1
```
### Weighted sampler (PyTorch)
```python
from torch.utils.data import WeightedRandomSampler
class_counts = [sum(y == c) for c in np.unique(y)]
weights = [1.0 / class_counts[label] for label in y]
sampler = WeightedRandomSampler(weights, num_samples=len(y), replacement=True)
loader = DataLoader(dataset, batch_size=32, sampler=sampler)
```
### Stratified split
```python
from sklearn.model_selection import StratifiedKFold
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
for train_idx, val_idx in skf.split(X, y):
train_fold(X[train_idx], y[train_idx])
```
### Borderline-SMOTE
```python
from imblearn.over_sampling import BorderlineSMOTE
bsm = BorderlineSMOTE(random_state=0)
X_res, y_res = bsm.fit_resample(X, y)
```
### Calibration check after handling
```python
from sklearn.calibration import calibration_curve
prob_true, prob_pred = calibration_curve(y_val, y_score, n_bins=10)
# 매 oversampling 매 의 의 calibration 의 distort
```
## 매 결정 기준
| 상황 | Approach |
|---|---|
| Mild (< 1:10) | Class weight |
| Moderate (1:10-1:100) | SMOTE / class weight |
| Severe (> 1:100) | Anomaly detection / focal |
| Tabular | XGBoost scale_pos_weight |
| DL | Focal loss + weighted sampler |
| Cost varies | Cost-sensitive |
**기본값**: 매 class weight + 매 threshold tune + 매 PR-AUC eval. 매 severe = focal + anomaly detection 의 explore. 매 SMOTE 는 careful (calibration distort).
## 🔗 Graph
- 부모: [[Machine-Learning]] · [[Data-Preprocessing]]
- 변형: [[SMOTE]] · [[Focal-Loss]]
- 응용: [[Anomaly-Detection]]
## 🤖 LLM 활용
**언제**: 매 fraud / medical / churn / anomaly.
**언제 X**: 매 balanced.
## ❌ 안티패턴
- **Accuracy metric on imbalanced**: 매 misleading.
- **SMOTE before train/val split**: 매 leakage.
- **No threshold tune**: 매 default 0.5 의 wrong.
- **Aggressive oversample**: 매 calibration 의 break.
- **Ignore minority cost**: 매 FN expensive.
## 🧪 검증 / 중복
- Verified (Chawla SMOTE 2002, He & Garcia review 2009, Lin focal 2017).
- 신뢰도 A.
## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — methods + 매 SMOTE / focal / threshold / scale_pos_weight code |