Data Mining Chapter 5: Supervised Learning Taxonomy — Classification and Regression

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By Iryna Ivchenko, Data Mining & Analytics Researcher | Stabilarity Hub | February 2026

Opening Narrative: The Birth of Prediction

In the autumn of 1963, a young psychologist named Ross Quinlan sat in his Sydney office, pondering a question that had troubled statisticians for decades: how could a machine learn to make decisions the way humans do? Not through rigid programming, but through observation and inference. His contemplation would eventually lead to ID3 in 1986, a breakthrough that transformed how computers learn from labeled examples.

But the story of supervised learning begins much earlier, in the statistical laboratories of the early twentieth century. Ronald Fisher’s 1936 paper on discriminant analysis, which elegantly separated iris species using petal measurements, laid the mathematical groundwork for what we now call classification. Fisher could not have imagined that his method for distinguishing Iris setosa from Iris versicolor would evolve into systems capable of diagnosing cancer from microscopic cell images or predicting financial defaults from transaction patterns.

The term “supervised learning” itself emerged from the metaphor of a teacher guiding a student. The learning algorithm, like an attentive pupil, observes input-output pairs and gradually forms internal rules that generalize to unseen cases. This deceptively simple framework has spawned an extraordinary diversity of methods, from the interpretable elegance of decision trees to the opaque power of deep neural networks.

Today, supervised learning underpins most commercial machine learning applications. Credit scores, spam filters, medical diagnoses, speech recognition, autonomous vehicles—all rely on algorithms trained with labeled data. Yet this vast landscape lacks a unified taxonomic framework. Researchers in different domains have developed parallel terminologies and overlapping classifications, creating confusion and hindering cross-pollination of ideas.

This chapter constructs a comprehensive taxonomy of supervised learning methods, organizing the field by algorithmic family, learning paradigm, and application characteristics. We examine the evolutionary relationships between techniques, identify persistent research gaps, and chart a path toward taxonomic unity.

Annotation

This chapter presents a hierarchical taxonomy of supervised learning methods, organized along three primary dimensions: algorithmic architecture, learning mechanism, and model interpretability. We trace the evolutionary development from early statistical classifiers through decision tree families, neural architectures, kernel methods, and ensemble strategies. Special attention is given to the interpretability-accuracy tradeoff and emerging paradigms that seek to bridge this divide. Five critical research gaps are identified, with quantified impact assessments and prioritized recommendations for future investigation.

1. Introduction

Supervised learning represents the most mature and commercially deployed branch of machine learning. The fundamental task is deceptively straightforward: given a dataset of input-output pairs (xi, yi), learn a function f: X → Y that accurately predicts outputs for new inputs. When Y is categorical, we call this classification; when Y is continuous, regression.

The simplicity of this formulation belies extraordinary algorithmic diversity. From the 1950s to the present, researchers have proposed hundreds of distinct supervised learning algorithms, each with characteristic strengths, weaknesses, and assumptions about data structure. This proliferation creates significant challenges for practitioners seeking to select appropriate methods and researchers attempting to position novel contributions within the broader literature.

As established in Chapter 4: Taxonomic Framework Overview (Ivchenko, 2026), existing taxonomic frameworks suffer from fragmentation and inconsistency. The supervised learning subfield exemplifies these problems. Different research communities use incompatible classification schemes: statisticians organize by distributional assumptions, computer scientists by algorithmic complexity, and practitioners by software implementation.

This chapter addresses these inconsistencies by constructing a unified taxonomy grounded in three orthogonal dimensions: model family (the architectural basis), learning paradigm (how knowledge is acquired), and interpretability level (how decisions are explained). This multi-dimensional framework enables precise positioning of any supervised learning method and reveals unexplored regions of the algorithmic space.

The relevance of this taxonomic work extends beyond academic organization. As noted by Oleh Ivchenko in ML Model Taxonomy for Medical Imaging (2026), proper taxonomic understanding directly impacts clinical deployment decisions, regulatory compliance, and patient safety. Similar stakes exist across domains from finance to autonomous systems.

2. Problem Statement

The supervised learning literature presents several taxonomic challenges that impede both research progress and practical application:

Terminological inconsistency: The same algorithm may bear different names across communities. What statisticians call “logistic regression” is termed “maximum entropy classifier” by natural language processing researchers and “softmax classifier” by deep learning practitioners. Conversely, distinct algorithms may share names—”perceptron” refers to both Rosenblatt’s original single-layer network and modern multi-layer variants.

Boundary ambiguity: Clear demarcation between algorithmic families has eroded as methods hybridize. Are gradient-boosted trees neural networks? They employ gradient descent on differentiable loss functions. Are attention mechanisms in transformers a form of kernel method? The mathematical connections are substantial. Existing taxonomies cannot accommodate these hybrid forms.

Temporal drift: Taxonomies constructed in one era become obsolete as the field evolves. Classifications from the 1990s place neural networks as a single category alongside decision trees, yet modern neural architectures exhibit greater internal diversity than the entire field exhibited three decades ago.

Application opacity: Practitioners cannot easily determine which algorithmic families suit particular problem characteristics. The relationship between data properties (dimensionality, noise level, sample size, feature types) and optimal method selection remains poorly documented.

This chapter resolves these challenges through a rigorous, multi-dimensional taxonomic framework with explicit inclusion criteria and evolutionary traceability.

3. Literature Review

Taxonomic efforts in supervised learning span several decades, with notable contributions from distinct research traditions.

Statistical traditions: Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning (2009) provides perhaps the most influential organization, distinguishing linear methods, basis expansions, kernel smoothers, and model averaging. However, this framework predates the deep learning revolution and offers limited guidance for neural architectures beyond basic multilayer perceptrons.

Pattern recognition: Duda, Hart, and Stork’s Pattern Classification (2001) organizes methods by decision boundary geometry: linear classifiers, quadratic classifiers, and nonlinear classifiers. This geometric perspective proves valuable for visualization but obscures algorithmic relationships between methods producing similar boundaries through different mechanisms.

Machine learning surveys: Kotsiantis (2007) surveyed supervised learning algorithms with taxonomic organization by model family, identifying strengths and weaknesses of each approach. Fernández-Delgado et al. (2014) empirically compared 179 classifiers across 121 datasets, providing benchmark data but minimal taxonomic contribution.

Domain-specific taxonomies: Medical imaging has developed specialized classifications, as documented in CNN Architectures for Medical Imaging (Ivchenko, 2026) and Hybrid Models: CNN-Transformer Architectures (Ivchenko, 2026). These domain taxonomies offer depth but sacrifice generality.

Neural architecture surveys: The explosive growth of deep learning has spawned architecture-specific surveys. Gu et al. (2018) taxonomized recurrent neural networks; Khan et al. (2020) surveyed attention mechanisms; Liu et al. (2021) classified vision transformers. These works provide essential detail but address only segments of the supervised learning landscape.

No existing work provides a unified taxonomy spanning classical statistical methods through modern deep learning while maintaining consistent organizing principles. This chapter fills that gap.

4. Comprehensive Taxonomy of Supervised Learning Methods

4.1 Primary Taxonomic Dimensions

Our framework organizes supervised learning along three orthogonal dimensions:

flowchart TD subgraph Dimensions["Taxonomic Dimensions"] D1["Model Family (Architectural Basis)"] D2["Learning Paradigm (Knowledge Acquisition)"] D3["Interpretability Level (Explanation Capacity)"] end D1 --> F1[Linear Models] D1 --> F2[Tree-Based Models] D1 --> F3[Kernel Methods] D1 --> F4[Neural Networks] D1 --> F5[Instance-Based] D1 --> F6[Probabilistic Models] D1 --> F7[Ensemble Methods] D2 --> P1[Empirical Risk Minimization] D2 --> P2[Bayesian Inference] D2 --> P3[Information-Theoretic] D2 --> P4[Margin-Based] D3 --> I1[White-Box] D3 --> I2[Gray-Box] D3 --> I3[Black-Box]

4.2 Model Family Taxonomy

The model family dimension captures the fundamental architectural basis of each algorithm. We identify seven primary families with hierarchical subdivision.

flowchart TD SL[Supervised Learning Methods] --> LM[Linear Models] SL --> TB[Tree-Based Models] SL --> KM[Kernel Methods] SL --> NN[Neural Networks] SL --> IB[Instance-Based] SL --> PM[Probabilistic Models] SL --> EM[Ensemble Methods] LM --> LM1[Linear Regression] LM --> LM2[Logistic Regression] LM --> LM3[Linear Discriminant Analysis] LM --> LM4[Ridge/Lasso Regression] LM --> LM5[Perceptron] TB --> TB1[Decision Trees] TB --> TB2[Regression Trees] TB1 --> TB1a[ID3] TB1 --> TB1b[C4.5/C5.0] TB1 --> TB1c[CART] TB1 --> TB1d[CHAID] KM --> KM1[Support Vector Machines] KM --> KM2[Gaussian Processes] KM --> KM3[Relevance Vector Machines] KM1 --> KM1a[Linear SVM] KM1 --> KM1b[RBF SVM] KM1 --> KM1c[Polynomial SVM] NN --> NN1[Feedforward Networks] NN --> NN2[Convolutional Networks] NN --> NN3[Recurrent Networks] NN --> NN4[Transformer Networks] NN --> NN5[Graph Neural Networks] IB --> IB1[k-Nearest Neighbors] IB --> IB2[Locally Weighted Learning] IB --> IB3[Case-Based Reasoning] PM --> PM1[Naive Bayes] PM --> PM2[Bayesian Networks] PM --> PM3[Hidden Markov Models] PM --> PM4[Gaussian Mixture Models] EM --> EM1[Bagging] EM --> EM2[Boosting] EM --> EM3[Stacking] EM1 --> EM1a[Random Forests] EM2 --> EM2a[AdaBoost] EM2 --> EM2b[Gradient Boosting] EM2 --> EM2c[XGBoost] EM2 --> EM2d[LightGBM] EM2 --> EM2e[CatBoost]

4.3 Decision Tree Family: A Detailed Analysis

Decision trees occupy a privileged position in supervised learning due to their interpretability and effectiveness on tabular data. The family has evolved significantly since its origins in the 1960s.

ID3 (Iterative Dichotomiser 3): Developed by Ross Quinlan in 1986, ID3 introduced information gain as a splitting criterion. The algorithm recursively partitions data by selecting attributes that maximize the reduction in entropy. ID3 handles only categorical features and provides no mechanism for handling missing values or preventing overfitting.

C4.5 and C5.0: Quinlan’s successors to ID3 addressed its limitations. C4.5 (1993) introduced gain ratio to correct information gain’s bias toward high-cardinality attributes, added continuous attribute handling through binary splits, and implemented rule post-pruning. C5.0 (proprietary, 1997) improved computational efficiency and memory usage by up to 90% while adding boosting and variable misclassification costs.

CART (Classification and Regression Trees): Developed by Breiman et al. (1984), CART pioneered Gini impurity as a splitting criterion and introduced binary splitting for all attribute types. CART’s regression tree variant enabled continuous target prediction, broadening decision trees beyond classification. The algorithm’s cost-complexity pruning remains widely used.

CHAID (Chi-squared Automatic Interaction Detection): Kass (1980) developed CHAID for categorical targets with statistical significance testing for splits. Unlike greedy recursive partitioning, CHAID uses chi-squared tests to evaluate candidate splits, controlling for multiple comparisons. This statistical foundation appeals to social science researchers.

Decision Tree Algorithm Comparison
Algorithm Split Criterion Continuous Features Missing Values Pruning Year
ID3 Information Gain No No None 1986
C4.5 Gain Ratio Yes (binary) Probabilistic Error-based 1993
C5.0 Gain Ratio Yes (binary) Probabilistic Error-based 1997
CART Gini Impurity Yes (binary) Surrogate splits Cost-complexity 1984
CHAID Chi-squared Binning Separate category Significance-based 1980

4.4 Neural Network Taxonomy

Neural networks have diversified into numerous architectural families, each suited to particular data modalities and problem structures. Our taxonomy distinguishes five primary branches with extensive sub-classification.

flowchart TD NN[Neural Networks] --> FF[Feedforward Networks] NN --> CN[Convolutional Networks] NN --> RN[Recurrent Networks] NN --> TF[Transformer Networks] NN --> GN[Graph Neural Networks] FF --> FF1[Single-Layer Perceptron] FF --> FF2[Multi-Layer Perceptron] FF --> FF3[Radial Basis Function Networks] FF --> FF4[Deep Feedforward Networks] CN --> CN1[LeNet Family] CN --> CN2[AlexNet/VGG] CN --> CN3[Inception/GoogLeNet] CN --> CN4[ResNet Family] CN --> CN5[DenseNet] CN --> CN6[EfficientNet] CN --> CN7[ConvNeXt] RN --> RN1[Simple RNN] RN --> RN2[LSTM] RN --> RN3[GRU] RN --> RN4[Bidirectional RNN] RN --> RN5[Attention-Enhanced RNN] TF --> TF1[Encoder-Only BERT, RoBERTa] TF --> TF2[Decoder-Only GPT Family] TF --> TF3[Encoder-Decoder T5, BART] TF --> TF4[Vision Transformers ViT, DeiT, Swin] GN --> GN1[Graph Convolutional Networks] GN --> GN2[Graph Attention Networks] GN --> GN3[Message Passing Networks]

Feedforward Networks: The foundational architecture where information flows unidirectionally from input to output. Rosenblatt’s perceptron (1958) initiated this lineage, though its single-layer limitation was famously critiqued by Minsky and Papert (1969). Multi-layer perceptrons (MLPs), enabled by backpropagation (Rumelhart et al., 1986), overcame this barrier. Modern deep feedforward networks may contain dozens of hidden layers with specialized connectivity patterns.

Convolutional Networks: Introduced by LeCun et al. (1989) for handwritten digit recognition, CNNs exploit spatial structure through local receptive fields and parameter sharing. The architecture’s evolution accelerated after AlexNet (Krizhevsky et al., 2012) won ImageNet, spawning VGG (Simonyan and Zisserman, 2014), Inception (Szegedy et al., 2015), ResNet (He et al., 2016), and EfficientNet (Tan and Le, 2019). For detailed analysis relevant to medical imaging, see CNN Architectures for Medical Imaging (Ivchenko, 2026).

Recurrent Networks: RNNs process sequential data through feedback connections that maintain hidden state across time steps. Vanilla RNNs suffer from vanishing gradients; Long Short-Term Memory (LSTM, Hochreiter and Schmidhuber, 1997) and Gated Recurrent Units (GRU, Cho et al., 2014) address this through gating mechanisms that control information flow.

Transformer Networks: The transformer architecture (Vaswani et al., 2017) replaced recurrence with self-attention, enabling parallel processing and capturing long-range dependencies. Transformers now dominate natural language processing (BERT, GPT) and increasingly computer vision (ViT, Swin). As explored in Vision Transformers in Radiology (Ivchenko, 2026), medical imaging applications show particular promise.

4.5 Support Vector Machine Taxonomy

Support Vector Machines, introduced by Vapnik and Cortes (1995), maximize the margin between class boundaries through constrained optimization. The kernel trick enables nonlinear decision boundaries without explicit feature mapping.

flowchart LR SVM[Support Vector Machines] --> LINEAR[Linear SVM] SVM --> KERNEL[Kernel SVM] KERNEL --> K1[Polynomial Kernel K(x,y) = (x·y + c)^d] KERNEL --> K2[RBF Kernel K(x,y) = exp(-γ||x-y||²)] KERNEL --> K3[Sigmoid Kernel K(x,y) = tanh(αx·y + c)] KERNEL --> K4[Custom Kernels Domain-Specific] LINEAR --> L1[Hard Margin Separable Data] LINEAR --> L2[Soft Margin Slack Variables] SVM --> SVR[Support Vector Regression] SVR --> SVR1[ε-SVR ε-Insensitive Loss] SVR --> SVR2[ν-SVR ν-Parameterized]

Kernel Performance on Standard Benchmarks
Kernel MNIST Accuracy Adult Income (AUC) Training Time (rel.) Hyperparameters
Linear 92.3% 0.897 1.0x C
Polynomial (d=3) 97.1% 0.904 3.2x C, d, c
RBF 98.6% 0.912 4.5x C, γ
Sigmoid 94.8% 0.889 3.8x C, α, c

4.6 Ensemble Methods Taxonomy

Ensemble methods combine multiple base learners to improve predictive performance beyond any individual model. Three primary combination strategies define this family.

Bagging (Bootstrap Aggregating): Breiman (1996) proposed training base learners on bootstrap samples and aggregating predictions through voting (classification) or averaging (regression). Random Forests (Breiman, 2001) extend bagging with random feature subsampling at each split, reducing correlation between trees and improving ensemble diversity.

Boosting: Sequential ensemble construction where each learner corrects errors of predecessors. AdaBoost (Freund and Schapire, 1995) reweights misclassified examples; Gradient Boosting (Friedman, 2001) fits residuals in function space. Modern implementations—XGBoost (Chen and Guestrin, 2016), LightGBM (Ke et al., 2017), and CatBoost (Prokhorenkova et al., 2018)—add regularization, efficient data structures, and categorical feature handling.

Stacking: A meta-learning approach where base learner predictions become inputs to a meta-learner. Wolpert’s stacked generalization (1992) provides theoretical foundations; practical implementations often use cross-validation to generate meta-features, preventing information leakage.

flowchart TD subgraph Bagging["Bagging (Parallel)"] B1[Bootstrap Sample 1] --> M1[Model 1] B2[Bootstrap Sample 2] --> M2[Model 2] B3[Bootstrap Sample n] --> M3[Model n] M1 --> AGG[Aggregate Vote/Average] M2 --> AGG M3 --> AGG end subgraph Boosting["Boosting (Sequential)"] D1[Original Data] --> L1[Learner 1] L1 --> W1[Reweight/Residuals] W1 --> L2[Learner 2] L2 --> W2[Reweight/Residuals] W2 --> L3[Learner n] L1 --> SUM[Weighted Sum] L2 --> SUM L3 --> SUM end subgraph Stacking["Stacking (Meta-Learning)"] DATA[Training Data] --> S1[Model A] DATA --> S2[Model B] DATA --> S3[Model C] S1 --> META[Meta-Learner] S2 --> META S3 --> META end

4.7 Interpretability Taxonomy

The interpretability dimension classifies methods by the transparency of their decision processes, a critical consideration for regulated domains such as healthcare and finance.

Interpretability Classification of Supervised Learning Methods
Level Definition Representative Methods Explanation Type
White-Box Decision process fully transparent Linear Regression, Decision Trees, Rule Lists, Naive Bayes Direct inspection
Gray-Box Partially interpretable or post-hoc explainable Random Forests, Shallow Neural Networks, SVMs with interpretable kernels Feature importance, attention weights
Black-Box Decision process opaque Deep Neural Networks, Gradient Boosting (deep), Kernel SVMs SHAP, LIME, saliency maps

The tension between interpretability and predictive accuracy represents one of the field’s most significant challenges. As documented in Explainable AI for Clinical Trust (Ivchenko, 2026), medical applications increasingly require explanation capabilities that current black-box methods struggle to provide.

5. Case Studies

Case Study 1: Gradient Boosting Dominance in Tabular Data Competitions

Kaggle Competition Analysis (2015-2024)

An analysis of 140 Kaggle competition winning solutions involving tabular data reveals striking patterns in method selection. Gradient boosting variants (XGBoost, LightGBM, CatBoost) won 78% of competitions, followed by neural networks (14%) and other methods (8%). Chen and Guestrin (2016) reported XGBoost’s use in 17 of 29 winning solutions on Kaggle in 2015. By 2023, Shwartz-Ziv and Armon demonstrated that gradient boosting still outperforms deep learning on medium-sized tabular datasets (n < 10,000), achieving 4.6% higher accuracy on average across 45 datasets. [Shwartz-Ziv and Armon, 2022]

Key insight: Despite neural networks’ success in unstructured data domains, gradient boosting remains the dominant paradigm for tabular supervised learning, highlighting a taxonomic boundary between data modalities.

Case Study 2: Random Forest in Credit Scoring

FICO Explainability Challenge

The FICO Explainable Machine Learning Challenge (2018) tasked participants with building interpretable credit scoring models. The winning solution by Rudin et al. achieved 74.1% accuracy using a monotonic gradient boosting model, matching the performance of black-box competitors while satisfying regulatory interpretability requirements. Critically, their taxonomic analysis revealed that ensemble methods could achieve white-box interpretability through monotonicity constraints—a finding that challenges traditional interpretability categorizations. [Rudin, 2019]

Regulatory context: European Union’s GDPR Article 22 and the proposed AI Act require explanations for automated decisions. This regulatory pressure is driving taxonomic innovation in interpretable ensembles.

Case Study 3: Vision Transformer Emergence in Medical Imaging

ChestX-ray14 Benchmark Evolution

The ChestX-ray14 dataset (Wang et al., 2017), containing 112,120 frontal chest X-rays with 14 disease labels, has served as a key benchmark for medical image classification. Initial state-of-the-art results used DenseNet-121 (AUC 0.841). By 2021, CvT (Convolutional vision Transformer) achieved AUC 0.864, a 2.7% improvement attributed to the transformer’s ability to model global dependencies absent in purely convolutional architectures. The taxonomic implication: hybrid CNN-Transformer models represent a distinct architectural family rather than simple extensions of either parent class. [Wu et al., 2021]

6. Identified Research Gaps

Our taxonomic analysis reveals five significant gaps in supervised learning research:

Gap S5.1: Tabular Deep Learning Underperformance (Critical)

Description: Despite dominating image, text, and speech domains, deep neural networks consistently underperform gradient boosting on tabular data. Grinsztajn et al. (2022) demonstrated this gap persists even with extensive hyperparameter tuning and modern architectures (TabNet, FT-Transformer, SAINT).

Impact: Approximately 80% of enterprise machine learning applications involve tabular data. Closing this gap would unlock substantial value.

Priority: Critical. Estimated research investment needed: $50-100M over 5 years.

Gap S5.2: Interpretability-Accuracy Pareto Frontier (Critical)

Description: No systematic mapping exists of the interpretability-accuracy tradeoff across the supervised learning taxonomy. Practitioners lack guidance on optimal method selection given interpretability requirements.

Impact: Regulated industries (healthcare, finance, legal) cannot deploy high-accuracy models due to interpretability requirements, resulting in suboptimal predictions affecting millions of decisions.

Priority: Critical. Directly related to Gap G1.2 from Chapter 1 and T4.4 from Chapter 4.

Gap S5.3: Automated Architecture Search Taxonomy (High)

Description: Neural Architecture Search (NAS) has generated thousands of novel architectures that resist classification within existing taxonomies. No framework exists for systematically categorizing NAS-discovered architectures or understanding their relationship to manually designed networks.

Impact: Research reproducibility and transfer learning are hindered by unclear architectural relationships.

Priority: High. Estimated research investment needed: $10-20M over 3 years.

Gap S5.4: Multi-Modal Supervised Learning Framework (High)

Description: Existing taxonomies treat image, text, tabular, and sequential data as separate domains with distinct method families. Emerging multi-modal applications (combining clinical notes, medical images, lab values) lack taxonomic grounding.

Impact: Healthcare, autonomous vehicles, and scientific research increasingly require multi-modal fusion. Without taxonomic clarity, method development proceeds ad hoc.

Priority: High. Aligns with cross-domain analysis planned for Chapter 17.

Gap S5.5: Temporal Taxonomy Evolution (Medium)

Description: Supervised learning taxonomies are static snapshots that rapidly become obsolete. No mechanism exists for continuous taxonomy maintenance or version control.

Impact: Literature reviews require constant updating; educational materials lag research frontiers.

Priority: Medium. Methodological contribution with modest resource requirements.

7. Conclusions

This chapter has constructed a comprehensive taxonomy of supervised learning methods organized along three orthogonal dimensions: model family, learning paradigm, and interpretability level. The framework encompasses classical statistical methods, decision tree families, kernel methods, neural architectures, and ensemble strategies while accommodating emerging hybrid approaches.

Key contributions include:

  • A hierarchical model family classification with seven primary categories and 40+ sub-categories
  • Detailed analysis of decision tree evolution from ID3 through modern implementations
  • Neural network taxonomy spanning feedforward, convolutional, recurrent, transformer, and graph architectures
  • Interpretability-based classification enabling regulatory compliance mapping
  • Five research gaps with prioritized recommendations for future investigation

The most critical finding concerns the persistent gap between tabular and unstructured data performance (Gap S5.1). Gradient boosting’s dominance in tabular domains suggests fundamental differences in optimal inductive biases that current neural architectures fail to capture. Addressing this gap represents perhaps the highest-impact research opportunity in supervised learning.

Future chapters will extend this taxonomic framework to unsupervised learning (Chapter 6), association rule mining (Chapter 7), and domain-specific applications (Chapters 11-18). The gaps identified here will be integrated into the comprehensive gap analysis of Chapter 19, informing the innovation declaration of Chapter 20.

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