Objectives and topics

Computational intelligence (CI) have gained great attention of scientific community over the last several years, Various models have been theoretically and empirically shown to provide significantly better performance than their single base models

Computational intelligence (CI) have emerged about two decades ago as an alternative to the traditional artificial intelligence (AI) paradigm. Early on, its focus was from a physical engineering perspective where the goal was to integrate the flexibility of judgment and response into conventional software programs and to create a computing environment that had immediate access to extensive databases and the capacities to learn and adapt. Given the successful development of CI in physical engineering, particularly in areas like pattern recognition and nonlinear forecasting, it was not long before economists like Halbert White were experimenting with economic predictions using neural networks and other CI technologies, and the era of economic and financial engineering (E&FE) began.

Of course, economics and finance presented a unique set of problems from a CI perspective so it generally was not possible to simply port solutions over from physical engineering without first modifying them. In this context, important E&FE modeling considerations include the heuristic nature of the approach, the emphasis on nonlinear relationships, data issues, domain knowledge, and behavioral changes.

Ensemble methods have gained great attention of scientific community over the last several years. Multiple models have been theoretically and empirically shown to provide significantly better performance than their single base models. Ensemble algorithms have found their application in various real word problems ranging from person recognition through medical diagnosis and text classification to financial forecasting.

The MCIIDEF 2026 Special Session at the 15th Conference on Information Technology and Its Applications (CITA) is devoted to the ensemble methods addressing classification, prediction, and clustering problems and their application to Big Data and small data sets as well as data streams and stationary data sets. We want to offer an opportunity for researchers and practitioners to identify new promising research directions as well as to publish recent advances in this area. The scope of the MCIIDEF 2026 includes, but is not limited to the following topics:

• AI and Big Data Technologies in Economy
• Financial Machine Learning and Data Mining
• Probabilistic Modeling/Inference
• Fuzzy Sets, Grey theory, Rough Sets, & Granular Computing
• Evolutionary Computation
• Soft Computation
• Time Series Analysis
• Non-linear Dynamics
• Nonlinear Forecasting
• Financial Data Mining
• Predictive Modeling and Forecasting
• Financial Engineering & Economics Applications
• Algorithmic and Quantitative Trading
• Portfolio Optimization and Asset Allocation
• Risk Management
• Pricing of Structured Securities
• Merge & Acqual Strategies
• Risk Arbitrage
• Behavioral Finance
• Agent-based Computational Economics
• Artificial and Emerging Markets
• Operations Research and Management Sciences
• Theoretical framework for ensemble methods
• Sub-sampling and feature selection in multiple model machine learning
• Homogeneous and heterogeneous prediction
• Hybrid methods in prediction and classification
• Implementations of ensemble learning algorithms
• Assessment and statistical analysis of ensemble models
• Applications of intelligence computation in digital economy.

Session Chairs

Chair:

Dr. Van - Thanh Phan, Vietnam - Korea University of Information and Communication Technology, the University of Danang, Danang, Vietnam

Email: pvthanh@vku.udn.vn

Co-Chairs:

Prof. Dr. Chia - Nan Wang, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

Email: cn.wang@nkust.edu.tw

Dr. Phi Hung Nguyen, FPT University, Hanoi, Vietnam

Email: Hungnp30@fe.edu.vn

Dr. Thanh Tung Trinh, Hanoi School of Business and Management, Vietnam National University, Hanoi, Vietnam

Email: tungtt@hsb.edu.vn

Dr. Nhat-Luong Nhieu, College of Technology and Design, University of Economics Ho Chi Minh City (UEH), Ho Chi Minh, Vietnam

Email:Luongnn@ueh.edu.vn

Session Committee Program

Prof. Dr. Chia - Nan Wang , National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan,

Dr. Van - Thanh Phan, Vietnam- Korea University of Information and Communication Technology, the University of Danang, Danang, Vietnam

Dr. Phi Hung Nguyen, FPT University, Hanoi, Vietnam

Dr. Thanh Tung Trinh, Hanoi School of Business and Management, Vietnam National University, Hanoi, Vietnam

Dr. Nhat Luong Nhieu, College of Technology and Design, University of Economics Ho Chi Minh City (UEH), Ho Chi Minh, Vietnam

Dr. Thanh Tuan Dang, Hong Bang University, Dongnai, Vietnam

Contact:

Dr. Van Thanh Phan

Affiliation: Faculty of Digital Economy and E-Commerce, Vietnam - Korea University of Information and Communication Technology, the University of Danang, Danang, Vietnam

Email: pvthanh@vku.udn.vn