哪种机器学习算法可完成两个数据集的匹配对查找？

Preprocessing & Feature Engineering (Critical First Steps) #

Before choosing an algorithm, fix the root causes of manual pairing pain points and build meaningful features:

• Clean text fields: Normalize reference numbers and bank补充信息 by removing spaces, special characters, and standardizing case (e.g., all uppercase) to eliminate spacing-related mismatches upfront.
• Handle amount discrepancies: Create features like absolute_amount_diff (absolute value of the difference between dataset 1 and dataset 2 amounts) and percentage_amount_diff. Add a binary feature amount_close_enough (1 if the difference is ≤ 0.02, 0 otherwise) to flag cent-level mismatches.
• Date alignment: Include features like exact_date_match (1 if dates are identical) and date_window_match (1 if dates are within ±1 day, accounting for delayed transaction posting).
• Text similarity: Compute metrics like Levenshtein edit distance (number of changes needed to match two strings) and cosine similarity (using TF-IDF or basic embeddings) between cleaned reference numbers and bank补充信息.

Recommended Algorithms #

Logistic Regression (Baseline) #

Start here to establish a performance benchmark. It’s fast, simple, and highly interpretable—you’ll see exactly which features (e.g., reference number similarity) drive match decisions. Ideal for validating if your feature set is effective before moving to complex models.

Tree-Based Models (Random Forest, XGBoost, LightGBM) #

These are the go-to choice for most record linkage tasks. They excel at capturing non-linear relationships between features (e.g., "high text similarity + tiny amount difference = match"). Tools like XGBoost and LightGBM handle imbalanced datasets (most pairs are non-matches) natively, and they provide feature importance scores to refine your feature set. This will likely deliver the best balance of performance and ease of use.

Support Vector Machines (SVM) #

Use this if you’re working with high-dimensional text features (like embeddings of long bank补充信息 fields). SVMs with an RBF kernel can model complex similarity patterns effectively, but they’re less interpretable than tree models—reserve this for cases where simpler models fall short.

Siamese Neural Networks (Advanced) #

For scenarios where reference numbers are buried in unstructured text, siamese networks learn contextual embeddings of text pairs to predict matches. This requires a large labeled dataset and more computational resources, so only consider it if the above models don’t meet your performance needs.

Key Training Tips #

• Labeled Data: Use your existing manually matched pairs as positive examples. For negatives, include clearly non-matching pairs (different dates + amounts) plus "hard" negatives (similar amounts but different references) to make the model robust.
• Evaluation: Avoid accuracy (misleading for imbalanced data). Use precision (how many predicted matches are real), recall (how many real matches are found), and F1-score (balance of precision and recall).
• Imbalance Handling: Use class weights in tree-based models, oversample positive examples, or undersample negative examples to avoid bias toward non-matches.

内容的提问来源于stack exchange，提问作者reikje