MengChu Zhou

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification

2016 · IEEE Transactions on Cybernetics

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always …

Private information retrieval in vehicular location-based services

2018

Acting as a new type of mobile terminals, vehicles are able to access Internet in real-time. Consequently, a specific kind of Location-Based Services (LBS), usually named Vehicular LBS (VLBS), has received significant attention because of …

An Adaptive Pre-clustering Support Vector Machine for Binary Imbalanced Classification

2018

Imbalance classification is a common but critical problem in machine learning and artificial intelligence. Derived from structural risk minimization, a Support Vector Machine (SVM) enjoys great reputation in classification. However, the original SVM is not …

Bi-objective Intelligent Task Scheduling for Green Clouds with Deep Learning-based Prediction

2020

The ever-increasing deployment of cloud data centers causes high energy consumption, high cost, and harmful environmental pollution. To solve above problems, cloud service providers are actively exploring to use green cloud data centers (GCDCs) by …

On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems

2021 · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences

Symbiotic autonomous systems (SAS) are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered …

Time-Aware Attention-Based Gated Network for Credit Card Fraud Detection by Extracting Transactional Behaviors

2022 · IEEE Transactions on Computational Social Systems

With the popularity of credit cards worldwide, timely and accurate fraud detection has become critically important to ensure the safety of their user accounts. Existing models generally utilize original features or manually aggregated features as …

Optimized Blockchain Sharding Model Based on Node Trust and Allocation

2023 · IEEE Transactions on Network and Service Management

Sharding technology is a promising solution for improving the scalability of blockchain systems. However, it faces the problem of allocating suitable trusted nodes into separate shards to satisfy security and efficiency requirements. Existing blockchain sharding …

Global Wagon Web: Enabling a Semantic-Enhanced Framework for Vehicle Infrastructure Cooperative System

2023

The core technology of Vehicle Infrastructure Cooperative System (VICS) has progressed to a “cooperative perception” phase, which necessitates an efficient, reliable and cost-effective information sharing mechanism. However, existing approaches are insufficient to meet the requirements …

A Spatial-temporal Gated Network for Credit Card Fraud Detection

2023

Timely and accurate credit card fraud detection (CCFD) is concerned by all financial institutions. Existing CCFD methods generally employ aggregated or raw features as their representations to train their detection models. Yet such features tend …

Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing Systems

2024

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. …

An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems

2015 · IEEE Transactions on Industrial Informatics

Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to …

Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models

2015 · IEEE Transactions on Neural Networks and Learning Systems

Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work …

A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

2015 · IEEE Transactions on Neural Networks and Learning Systems

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and …

A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection

2021 · IEEE Transactions on Systems Man and Cybernetics Systems

Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data. However, this assumption does …