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🔥 Content Introduction
1. Background and Advantages of Technology Integration
1.1 Advantages of Transformer Encoder
The Transformer encoder, based on the self-attention mechanism, can process input sequences in parallel, effectively capturing long-distance dependencies, and has shown excellent performance in fields such as natural language processing and time series forecasting. For complex data (such as multivariate time series and image features), it can extract features from different dimensions through the multi-head attention mechanism, achieving higher computational efficiency than traditional recurrent neural networks (like LSTM) when handling long sequences, and avoiding the vanishing gradient problem.
1.2 Role of SHAP Analysis
SHAP values, based on the Shapley value concept from game theory, quantify the contribution of each input feature to the model output. By calculating the SHAP values of features, it is possible to clearly explain the basis of model predictions, for example, in prediction tasks, determining which features drive the increase or decrease of predicted values, thus addressing the “black box” problem of deep learning models.
1.3 Innovations from Combining Both
Applying the Transformer encoder to prediction tasks (such as regression and classification), and then utilizing SHAP analysis to explore the internal feature interactions and decision logic of the encoder, can achieve the dual objectives of “high-precision prediction + transparent decision-making”. For instance, in time series forecasting, it can leverage the Transformer to capture complex patterns in sequences while using SHAP values to reveal the impact of different variables at different times on the prediction results, providing a basis for model optimization and business decision-making.
2. Model Construction and SHAP Analysis Process
2.1 Model Construction Based on Transformer Encoder
Taking time series forecasting as an example (such as power load forecasting, stock price forecasting):
- Data Preprocessing: Normalize input data, fill in missing values, etc., converting the time series into fixed-length sequence segments (e.g., using the past 100 time steps as input to predict the next time step).
- Transformer Encoder Structure:
- Input Layer: Maps the sequence to a high-dimensional embedding space (e.g., through an Embedding layer);
- Multi-Head Attention Layer: Computes multiple attention heads in parallel to capture feature dependencies from different dimensions;
- Feedforward Neural Network Layer: Applies nonlinear transformations to the attention output;
- Output Layer: Outputs prediction results based on the task type (regression or classification).
2.2 Steps to Implement SHAP Analysis
- Calculate SHAP Values: Use the SHAP library (e.g.,
<span>shap</span>package) to analyze the trained Transformer model. SHAP efficiently calculates the SHAP values of each feature in each sample using approximation algorithms (e.g., TreeSHAP, KernelSHAP). - Visualization Analysis:
- SHAP Summary Plot: Displays the distribution of SHAP values for each feature across all samples, allowing for intuitive assessment of which features significantly impact model output and the positive/negative correlation of features with output.
- SHAP Dependence Plot: Analyzes the relationship between the SHAP values of a single feature and its feature values, revealing the nonlinear impact of features on prediction results.
- SHAP Force Plot: For a single sample, visualizes how each feature’s SHAP value drives the prediction result in a certain direction, helping to understand the model’s decision logic for specific samples.
⛳️ Running Results
📣 Sample Code
🔗 References
🎈 Some theoretical references are from online literature; if there is any infringement, please contact the author for removal.
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🌈 Various intelligent optimization algorithm improvements and applications
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🌈 Machine learning and deep learning time series, regression, classification, clustering, and dimensionality reduction
2.1 BP time series, regression prediction, and classification
2.2 ENS voice neural network time series, regression prediction, and classification
2.3 SVM/CNN-SVM/LSSVM/RVM support vector machine series time series, regression prediction, and classification
2.4 CNN|TCN|GCN convolutional neural network series time series, regression prediction, and classification
2.5 ELM/KELM/RELM/DELM extreme learning machine series time series, regression prediction, and classification
2.6 GRU/Bi-GRU/CNN-GRU/CNN-BiGRU gated neural network time series, regression prediction, and classification
2.7 Elman recurrent neural network time series, regression prediction, and classification
2.8 LSTM/BiLSTM/CNN-LSTM/CNN-BiLSTM long short-term memory neural network series time series, regression prediction, and classification
2.9 RBF radial basis neural network time series, regression prediction, and classification
2.10 DBN deep belief network time series, regression prediction, and classification
2.11 FNN fuzzy neural network time series, regression prediction
2.12 RF random forest time series, regression prediction, and classification
2.13 BLS broad learning time series, regression prediction, and classification
2.14 PNN pulse neural network classification
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2.16 Time series, regression prediction, and classification
2.17 Time series, regression prediction, and classification
2.18 XGBOOST ensemble learning time series, regression prediction, and classification
2.19 Transform various combinations time series, regression prediction, and classification
Covering directions include wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load forecasting, stock price prediction, PM2.5 concentration prediction, battery health state prediction, electricity consumption prediction, water body optical parameter inversion, NLOS signal identification, precise prediction of subway stops, transformer fault diagnosis
🌈 In image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing
🌈 In path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), drone three-dimensional path planning, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problems, electric vehicle routing planning (EVRP), two-layer vehicle routing planning (2E-VRP), hybrid vehicle routing planning, ship trajectory planning, full path planning, warehouse patrol
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Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signals, EEG signals, signal timing optimization, ECG signals, DOA estimation, encoding and decoding, variational mode decomposition, pipeline leakage, filters, digital signal processing + transmission + analysis + denoising, digital signal modulation, bit error rate, signal estimation, DTMF, signal detection
🌈 In power systems
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🌈 In cellular automata
Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion
🌈 In radar
Kalman filter tracking, trajectory association, trajectory fusion, SOC estimation, array optimization, NLOS identification
🌈 In workshop scheduling
Zero-wait flow shop scheduling problem (NWFSP), Permutation flow shop scheduling problem (PFSP), Hybrid flow shop scheduling problem (HFSP), zero idle flow shop scheduling problem (NIFSP), distributed permutation flow shop scheduling problem (DPFSP), blocking flow shop scheduling problem (BFSP)
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