Iterative Feature Space Optimization through Incremental Adaptive Evaluation

2025-01-24

AI Summary

Existing feature-space optimization methods suffer from three key limitations: evaluation bias, model-specific overfitting, and inefficient retraining of evaluators. To address these, we propose EASE—a general-purpose, adaptive feature-space evaluation framework. EASE introduces two novel components: (i) a feature–sample subspace decoupling mechanism for disentangled representation generation, and (ii) a context-aware, incremental attention-based evaluator that enables hard-sample focusing, cross-model generalization, and continual knowledge accumulation. Methodologically, EASE integrates feature importance scoring, challenging sample identification, weighted shared multi-head attention encoding, and incremental parameter updates. Evaluated on 14 real-world datasets, EASE consistently improves downstream task performance, achieves a 3.2× speedup in evaluation efficiency, and substantially outperforms state-of-the-art methods in generalization and robustness.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Search and Optimization: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Iterative feature space optimization involves systematically evaluating and adjusting the feature space to improve downstream task performance. However, existing works suffer from three key limitations:1) overlooking differences among data samples leads to evaluation bias; 2) tailoring feature spaces to specific machine learning models results in overfitting and poor generalization; 3) requiring the evaluator to be retrained from scratch during each optimization iteration significantly reduces the overall efficiency of the optimization process. To bridge these gaps, we propose a gEneralized Adaptive feature Space Evaluator (EASE) to efficiently produce optimal and generalized feature spaces. This framework consists of two key components: Feature-Sample Subspace Generator and Contextual Attention Evaluator. The first component aims to decouple the information distribution within the feature space to mitigate evaluation bias. To achieve this, we first identify features most relevant to prediction tasks and samples most challenging for evaluation based on feedback from the subsequent evaluator. This decoupling strategy makes the evaluator consistently target the most challenging aspects of the feature space. The second component intends to incrementally capture evolving patterns of the feature space for efficient evaluation. We propose a weighted-sharing multi-head attention mechanism to encode key characteristics of the feature space into an embedding vector for evaluation. Moreover, the evaluator is updated incrementally, retaining prior evaluation knowledge while incorporating new insights, as consecutive feature spaces during the optimization process share partial information. Extensive experiments on fourteen real-world datasets demonstrate the effectiveness of the proposed framework. Our code and data are publicly available.

Problem

• Feature Space Optimization
• Model Overfitting
• Inefficient Training and Evaluation

Innovation

• EASE
• Adaptive Feature Space Evaluation
• Optimization Efficiency