arXiv subjects

D. Frank Hsu

Publications and source records attributed to D. Frank Hsu.

8 recordsLinked to original sources

arXiv2026

Contextual Value Alignment via Multilayer Combinatorial Fusion

Aligning large language models (LLMs) with human values remains a major challenge, especially for trustworthy AI. While existing approaches such as RLHF, CAI, and their variants have achieved promising results, they often rely on a single-agent framework and a unified reward system. This limits their ability to capture ethical pluralism, adapt to diverse moral contexts, and reflect the dynamics of multi-agent moral reasoning. In this work, we propose a framework that utilizes multilayer combinatorial fusion for contextual value alignment (MCF-CVA). At the first layer of the framework, it instantiates multiple moral agents, each fine-tuned to represent a distinctive value. Their outputs are then expanded combinatorially using both score- and rank-combinations as well as average and weighted aggregations. These combined models are then reduced to the same number of initial moral agents. This expansion and reduction (EAR) process continues for multi-layers until a stopping criterion is reached. The MCF-CVA framework leverages cognitive diversity between agents to mitigate conflicts and redundancies across multiple agents, producing responses that better reflect contextual human values. The framework using the EAR algorithm is performed on the dual architecture of Euclidean score space and Kemeny rank space. Empirical evaluations demonstrated that the proposed framework outperforms single-agent baselines, multi-agent single-layer results, and previous aggregation approaches on standard metrics, showing that the MCF-CVA framework provides a robust and effective mechanism for advancing contextual value alignment in LLMs.

cs.AI↗

arXiv2026

NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysis

Machine learning models have demonstrated remarkable success in sports prediction in the past years, often treating sports prediction as a classification task within the field. This paper introduces new perspectives for analyzing sports data to predict outcomes more accurately. We leverage rankings to generate team rankings for the 2024 dataset using Combinatorial Fusion Analysis (CFA), a new paradigm for combining multiple scoring systems through the rank-score characteristic (RSC) function and cognitive diversity (CD). Our result based on rank combination with respect to team ranking has an accuracy rate of $74.60\%$, which is higher than the best of the ten popular public ranking systems ($73.02\%$). This exhibits the efficacy of CFA in enhancing the precision of sports prediction through different lens.

cs.LG↗

arXiv2026

Enhancing Value Alignment of LLMs with Multi-agent system and Combinatorial Fusion

Aligning large language models (LLMs) with human values is a central challenge for ensuring trustworthy and safe deployment. While existing methods such as Reinforcement Learning from Human Feedback (RLHF) and its variants have improved alignment, they often rely on a single evaluator or narrowly defined reward signals, limiting their ability to capture ethical pluralism. In this work, we propose the Value Alignment System using Combinatorial Fusion Analysis (VAS-CFA), a framework that operationalizes multi-agent fusion alignment. It instantiates multiple moral agents, each fine-tuned to represent a distinct normative perspective, and fuses their outputs using CFA with both rank- and score-based aggregation. This design leverages cognitive diversity, between agents, to mitigate conflicts and redundancies across multiple agents, producing responses that better reflect human values. Empirical evaluation demonstrates that VAS-CFA outperforms both single agent baselines and prior aggregation approaches on standard metrics, showing that multi-agent fusion provides a robust and effective mechanism for advancing value alignment in LLMs.

cs.MA↗

arXiv2026

Bitcoin Price Prediction using Machine Learning and Combinatorial Fusion Analysis

In this work, we propose to apply a new model fusion and learning paradigm, known as Combinatorial Fusion Analysis (CFA), to the field of Bitcoin price prediction. Price prediction of financial product has always been a big topic in finance, as the successful prediction of the price can yield significant profit. Every machine learning model has its own strength and weakness, which hinders progress toward robustness. CFA has been used to enhance models by leveraging rank-score characteristic (RSC) function and cognitive diversity in the combination of a moderate set of diverse and relatively well-performed models. Our method utilizes both score and rank combinations as well as other weighted combination techniques. Key metrics such as RMSE and MAPE are used to evaluate our methodology performance. Our proposal presents a notable MAPE performance of 0.19\%. The proposed method greatly improves upon individual model performance, as well as outperforms other Bitcoin price prediction models.

q-fin.ST↗

arXiv2026

A Model Fusion Approach for Enhancing Credit Approval Decision Making

Credit default poses significant challenges to financial institutions and consumers, resulting in substantial financial losses and diminished trust. As such, credit default risk management has been a critical topic in the financial industry. In this paper, we present Combinatorial Fusion Analysis (CFA), a model fusion framework, that combines multiple machine learning algorithms to detect and predict credit card approval with high accuracy. We present the design methodology and implementation using five pre-trained models. The CFA results show an accuracy of 89.13% which is better than conventional machine learning and ensemble methods.

cs.CE↗

arXiv2026

Enhancing SDG-Text Classification with Combinatorial Fusion Analysis and Generative AI

(Natural Language Processing) NLP techniques such as text classification and topic discovery are very useful in many application areas including information retrieval, knowledge discovery, policy formulation, and decision-making. However, it remains a challenging problem in cases where the categories are unavailable, difficult to differentiate, or are interrelated. Social analysis with human context is an area that can benefit from text classification, as it relies substantially on text data. The focus of this paper is to enhance the classification of text according to the UN's Sustainable Development Goals (SDGs) by collecting and combining intelligence from multiple models. Combinatorial Fusion Analysis (CFA), a system fusion paradigm using a rank-score characteristic (RSC) function and cognitive diversity (CD), has been used to enhance classifier methods by combining a set of relatively good and mutually diverse classification models. We use a generative AI model to generate synthetic data for model training and then apply CFA to this classification task. The CFA technique achieves 96.73% performance, outperforming the best individual model. We compare the outcomes with those obtained from human domain experts. It is demonstrated that combining intelligence from multiple ML/AI models using CFA and getting input from human experts can, not only complement, but also enhance each other.

cs.CL↗

arXiv2025

Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion

This paper presents a novel approach to sentiment classification using the application of Combinatorial Fusion Analysis (CFA) to integrate an ensemble of diverse machine learning models, achieving state-of-the-art accuracy on the IMDB sentiment analysis dataset of 97.072\%. CFA leverages the concept of cognitive diversity, which utilizes rank-score characteristic functions to quantify the dissimilarity between models and strategically combine their predictions. This is in contrast to the common process of scaling the size of individual models, and thus is comparatively efficient in computing resource use. Experimental results also indicate that CFA outperforms traditional ensemble methods by effectively computing and employing model diversity. The approach in this paper implements the combination of a transformer-based model of the RoBERTa architecture with traditional machine learning models, including Random Forest, SVM, and XGBoost.

cs.LG↗

arXiv2023

Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis

Mitigating Denial-of-Service (DoS) attacks is vital for online service security and availability. While machine learning (ML) models are used for DoS attack detection, new strategies are needed to enhance their performance. We suggest an innovative method, combinatorial fusion, which combines multiple ML models using advanced algorithms. This includes score and rank combinations, weighted techniques, and diversity strength of scoring systems. Through rigorous evaluations, we demonstrate the effectiveness of this fusion approach, considering metrics like precision, recall, and F1-score. We address the challenge of low-profiled attack classification by fusing models to create a comprehensive solution. Our findings emphasize the potential of this approach to improve DoS attack detection and contribute to stronger defense mechanisms.

cs.CR↗