What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Genetic algorithm, Data mining, Artificial neural network and Mathematical optimization.
His Artificial intelligence research incorporates themes from Machine learning, Hybrid system and Pattern recognition.
Pei-Chann Chang focuses mostly in the field of Genetic algorithm, narrowing it down to matters related to Cluster analysis and, in some cases, Decision rule.
His Data mining study combines topics from a wide range of disciplines, such as Stock exchange and Fuzzy rule, Fuzzy control system, Fuzzy logic.
His Artificial neural network study integrates concerns from other disciplines, such as Demand forecasting, Operations research, Industrial engineering and Sales forecasting.
His work on Population-based incremental learning as part of general Mathematical optimization study is frequently connected to Job shop scheduling, Tardiness, Dynamic priority scheduling and Nurse scheduling problem, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His most cited work include:
- Kernel Sparse Representation-Based Classifier (270 citations)
- A TSK type fuzzy rule based system for stock price prediction (257 citations)
- One-machine rescheduling heuristics with efficiency and stability as criteria (219 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Mathematical optimization, Artificial intelligence, Genetic algorithm, Data mining and Job shop scheduling.
His work on Evolutionary algorithm as part of general Mathematical optimization research is frequently linked to Single-machine scheduling, Tardiness and Flow shop scheduling, bridging the gap between disciplines.
His Artificial intelligence course of study focuses on Machine learning and Quality.
Pei-Chann Chang has researched Genetic algorithm in several fields, including Multi-objective optimization, Travelling salesman problem and Crossover.
As a member of one scientific family, Pei-Chann Chang mostly works in the field of Data mining, focusing on Time series and, on occasion, Finance.
Pei-Chann Chang studied Job shop scheduling and Dynamic priority scheduling that intersect with Fair-share scheduling.
He most often published in these fields:
- Mathematical optimization (34.32%)
- Artificial intelligence (30.51%)
- Genetic algorithm (26.27%)
What were the highlights of his more recent work (between 2014-2020)?
- Data mining (25.85%)
- Mathematical optimization (34.32%)
- Artificial intelligence (30.51%)
In recent papers he was focusing on the following fields of study:
Pei-Chann Chang mostly deals with Data mining, Mathematical optimization, Artificial intelligence, Machine learning and Quality.
His studies deal with areas such as Feature selection, Collaborative filtering, Artificial immune system and Support vector machine as well as Data mining.
His work on Evolutionary algorithm, Particle swarm optimization and 2-opt as part of general Mathematical optimization research is often related to Job shop scheduling and Block, thus linking different fields of science.
His study explores the link between 2-opt and topics such as Bottleneck traveling salesman problem that cross with problems in Genetic algorithm.
His research in Genetic algorithm tackles topics such as Mixed model which are related to areas like Multi-objective optimization.
His Artificial intelligence research is multidisciplinary, relying on both Sample and Pattern recognition.
Between 2014 and 2020, his most popular works were:
- A block recombination approach to solve green vehicle routing problem (59 citations)
- A Takagi-Sugeno fuzzy model combined with a support vector regression for stock trading forecasting (48 citations)
- Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Mathematical optimization, Data mining, Evolutionary algorithm, Supply chain and Support vector machine.
In the field of Mathematical optimization, his study on Multi-objective optimization overlaps with subjects such as Job shop scheduling.
The Tardiness research Pei-Chann Chang does as part of his general Job shop scheduling study is frequently linked to other disciplines of science, such as Scheduling, therefore creating a link between diverse domains of science.
The Data mining study combines topics in areas such as Similarity, Recommender system, MovieLens and Pearson product-moment correlation coefficient.
His Evolutionary algorithm study combines topics in areas such as Estimation of distribution algorithm and Benchmark.
He has included themes like Genetic algorithm and Product in his Quality study.
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