Interpretable Feature Generation

Updated 14 November 2025

Interpretable feature generation is the creation of structured, human-understandable representations from raw data using symbolic, neural, and agent-based methods.
It leverages techniques such as relational transformations, neural bottlenecking, and LLM-driven pipelines to maintain semantic clarity and auditability.
Empirical studies indicate these methods can achieve competitive predictive accuracy while providing transparent, domain-aligned feature sets.

Interpretable feature generation is the purposeful creation of structured, semantically meaningful representations from raw or intermediate data such that both model developers and domain experts can readily audit, reason about, and act on the resulting features in machine learning systems. This concept underpins transparency and reliability in predictive modeling, as model interpretability is fundamentally limited by the comprehensibility of its input features. Techniques in this field span from symbolic and domain-driven transformations, through neural and reinforcement learning guided by explicit interpretability metrics, to modular agent-based pipelines that automate and validate feature construction against domain knowledge or user needs.

1. Principles and Taxonomy of Interpretability in Feature Generation

A foundational perspective in interpretable feature generation distinguishes the model-ready feature space from the interpretable feature space. Zytek et al. (Zytek et al., 2022) systematize this distinction by formalizing interpretability as a set of properties independent of predictive or modeling concerns. Their taxonomy includes:

Readable: Human-friendly names and units (e.g., “Age” instead of “x12”).
Human-Worded: Natural language phrasing (e.g., “Gender – Female”).
Understandable: Logical coherence without requiring statistical expertise.
Meaningful: Features reflect relationships known to the domain.
Trackable/Simulatable: Traceable to underlying data and reproducible via explicit formulas.
Abstract-Concept: Combination of observed variables via domain formulas (e.g., “Participation” as hours of videos plus assignment attempts).

Feature generation pipelines may also aim to maintain model compatibility and predictiveness, but interpretability properties are necessary for features to be useful in decision-making contexts or for regulatory auditing.

2. Symbolic, Relational, and Aggregation-Based Methods

Symbolic approaches generate interpretable features through transparent transformations, aggregation, and propositionalization. In the iFx pipeline for multivariate time-series regression (Gay et al., 2021), interpretable features are constructed by:

Storing multi-variate time series in a relational schema with diverse elementary transforms (e.g., first and second differences, autocorrelation, periodograms).
Applying simple aggregation (mean, std, min, max), selection (row subsetting by time/value predicates), and chaining of these operations to yield scalar features with explicit semantic meaning (e.g., standard deviation of the derivative of a variable during the first 10 time units).
Statistically filtering features via a Bayesian MAP objective, retaining only features with a robust, quantifiable association with the numeric target.

This strategy yields batch-extractable, human-auditable features such as “Sum(D_5, Value)” or “StdDev(Selection(TS_2, 5≤Time≤15), Value)” that align with classical time-series diagnostic logic.

3. Neural and Self-Supervised Methods with Embedded Interpretability

Neural architectures for interpretable feature generation employ structured constraints, explicit information allocation, or modularity to preserve end-to-end transparency.

Interpretable Feature Extractor (IFE) in Visual RL: Pham & Cangelosi (Pham et al., 14 Apr 2025) design a two-stage CNN architecture for deep reinforcement learning, enforcing precision in the spatial attention mask via Human-Understandable Encoding (HUE) with non-overlapping convolutions (kernel size $L$ , stride $S=L$ ). The one-to-one correspondence between spatial features and input patches yields masks $A(x,y)$ with zero spatial displacement, while a follow-up Agent-Friendly Encoding (AFE) recovers full-capacity representation learning. This structure generates attention masks that directly pinpoint “what” and “where” the agent focuses on, measurable and visualizable at the pixel (object) level.
Distributed Information Bottleneck (DIB): Hsu et al. (Murphy et al., 2022) compress each input feature $X_i$ into a bottleneck $Z_i$ , learning a parameterized encoder $p(z_i|x_i)$ that trades off minimal information $I(X_i;Z_i)$ with maximal predictive signal $I(Y;Z_{1:d})$ . By varying $\beta$ in the objective,

$\mathcal{L} = \beta \sum_{i=1}^d I(X_i;Z_i) - I(Y;Z_{1:d}),$

they induce a spectrum of models revealing how much information about each feature is critical for prediction, identifying interpretable clusters or bins along each feature axis. Analytical tools like Bhattacharyya coefficient matrices across feature values reveal exactly which raw distinctions are preserved at each $\beta$ setting.

Feature Leveling in Deep Networks: Lu & Yang (Lu et al., 2019) introduce feature-leveling architectures that learn binary gates at each layer, routing low-level features directly to a final General Linear Model (GLM) layer. This enables inspection of how each original or intermediate feature contributes to predictions, bridging standard DNNs with GLM-like transparency.

4. Automated, Knowledge-Guided, and Agent-Based Approaches

Recent methods leverage LLMs, knowledge graphs, and modular multi-agent architectures to automate the extraction, validation, and transformation of features in unstructured or semi-structured domains, enforcing interpretability through symbolic or ontological supervision.

SNOW: Agent-Based LLM Pipeline for EHR: SNOW (Wang et al., 3 Aug 2025) orchestrates five LLM-powered agents—Feature Discovery, Extraction, Validation, Post-processing, and Aggregation Code Generation—to autonomously generate a tabular set of clinically interpretable features from free-text clinical notes. Explicit variable names, domain-grounded extraction rules, and validation loops (mirroring the clinical feature abstraction process) ensure output features such as “max gleason score primary” and “percentage of positive cores” are domain-auditable and actionable. Unlike dense embedding approaches, SNOW’s features are explicitly listed, described, and formulaically aggregated (e.g., maximum, count, percent functions). SNOW matched the diagnostic accuracy of a year-long manual curation effort (AUC-ROC 0.761 vs. 0.771), demonstrating that agent-based LLM pipelines can deliver expert-level interpretable feature matrices.
Domain Knowledge and Reinforcement Learning: Frameworks such as SMART (Bouadi et al., 3 Oct 2024) and KRAFT (Bouadi et al., 1 Jun 2024) use knowledge graphs populated with Description Logic ontologies and SWRL rules to define interpretability constraints. These systems train reinforcement learning agents to generate features via allowed transformations, rewarding both downstream predictive gain and semantic evaluability by an ontological reasoner (e.g., “DURATION = AppointmentDate – ScheduledDate” is valid for Date types).

SMART and KRAFT explicitly represent state as a “semantic vector” $\Phi(\cdot)$ , mapping features to domain concepts. Transformation sequences are only executed and retained if resulting features are semantically validated. Interpretable features are thus rigorously enforced by domain axioms—for example, addition of distance units or ISO-8601 date operations but not addition across heterogeneous units. Empirical validation indicates such frameworks consistently outperform both random and non-semantic AutoFE baselines while preserving an average interpretability score $I_{KG}\geq0.75$ .

Dynamic LLM-Based Feature Generation: Approaches utilizing LLM agents and prompt strategies (Zhang et al., 4 Jun 2024, Balek et al., 11 Sep 2024) dynamically adapt feature extraction, explanation, and action mining over diverse tabular or textual inputs. These workflows require each feature’s algebraic or logical origin (“Chain-of-Thought” log) and cap allowable complexity, making it possible to produce compact, auditable features (e.g., “rigor”, “novelty”, “grammatical correctness” from LLaMA 2) for downstream classifiers without resorting to high-dimensional, uninterpretable embeddings. Action rule mining on such feature sets (via, e.g., Action-Apriori) further enables policy-relevant recommendations (“increase rigor from medium to high”) based purely on machine-extracted, human-readable feature representations.

5. Interpretability-Preserving Feature Generation for Specialized Data Modalities

Specialized interpretable feature generation strategies have emerged for domains beyond classical tabular or text data:

Interpretable Set Functions and Lattices: Cotter et al. (Cotter et al., 2018) construct deep lattice networks and semantic feature engines (SFE) that map variable-length sets of sparse categorical features (e.g., n-grams, actor sets) to dense monotonic aggregations, where each atomic function (calibrator, lattice) is visually and numerically inspectable. Monotonicity constraints (e.g., “increasing review length never decreases predicted sales”) can be encoded and globally enforced.
Audio and Visual Generative Models: Generative Invertible Networks (GIN) (Chen et al., 2018) achieve pathophysiologic interpretability by enforcing a bidirectional mapping between latent codes and images, so every axis in the latent vector $z$ has an explicit, observable meaning (e.g., valve rotation or calcification in CT slices). In ICGAN (Liu et al., 11 Jun 2024), audio GANs learn an implicit, continuous conditioning space where each axis can be morphed to effect smooth, interpretable transitions in timbre or sound class. Both frameworks depend on invertibility or explicit conditioning, in contrast to opaque autoencoders or CNNs, affording clinicians or sound designers practical control and feature-level understanding.
Attention Networks and Time Series Extraction: For temporal modeling, multi-head attention blocks, as in Wang et al. (Wang et al., 2022), are custom-structured so each “feature-engineering head” yields a scalar feature interpretable as a specific temporal or combination pattern. These networks preserve an explicit decomposition in the original variables, visualizable via instance-specific attention and gating heatmaps.

6. Validation, Performance, and Limitations

Interpretability-focused feature generation must be empirically validated to ensure that semantic clarity does not sacrifice predictive utility. Several systematic findings recur:

Quantitative Parity or Superiority: Interpretable feature generators have matched or exceeded baseline and black-box counterparts in AUC (e.g., SNOW 0.761 vs. CFG 0.771, all RFGs < 0.691 (Wang et al., 3 Aug 2025)), accuracy (KRAFT outperforming all baselines by 6.3% on average (Bouadi et al., 1 Jun 2024)), and regression/classification error (e.g., Spofe’s lower RMSE vs. KPCA/SKPCA (Zhang et al., 23 Mar 2025)).
Feature Set Compactness: Systems like LLM-based text feature extraction achieved near-parity with benchmarking embeddings (SciBERT 768-dim vs. LLM 62-dim, $\Delta$ F1=0.03 (Balek et al., 11 Sep 2024)) while retaining direct semantic attribution.
Domain Generality and Robustness: Knowledge-guided approaches and agent pipelines transfer to other data or clinical contexts by updating knowledge graphs, prompts, or validation rules.

However, major limitations include reliance on the coverage and quality of the domain knowledge base (SMART, KRAFT), computational cost for LLM- or agent-driven systems, dependency on the quality and biases of pretrained models, and potential bottlenecks in symbolic reasoning as knowledge graph size grows. For information-bottleneck and neural approaches, interpretability is constrained by the user’s ability to audit clusterings, confusion matrices, or the gating of feature-level signals—visualization tools become essential.

In conclusion, interpretable feature generation encompasses formal taxonomies, symbolic transformations, agent-based LLM pipelines, knowledge-graph-guided RL, self-supervised bottlenecking, and structure-imposing neural architectures. These strategies systematically embed semantic and epistemic clarity into machine learning workflows, directly supporting reliable, auditable, and actionable AI across clinical, scientific, time-series, and text domains (Pham et al., 14 Apr 2025, Wang et al., 3 Aug 2025, Zhang et al., 23 Mar 2025, Bouadi et al., 3 Oct 2024, Bouadi et al., 1 Jun 2024, Murphy et al., 2022, Zytek et al., 2022, Vouk et al., 2023).