What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial intelligence, Artificial neural network, Feature selection, Data mining and Machine learning.
His work in Artificial intelligence addresses issues such as Discretization, which are connected to fields such as Group method of data handling.
His work on Feedforward neural network, Backpropagation and Time delay neural network as part of general Artificial neural network research is often related to Linear function, thus linking different fields of science.
His Feature selection study improves the overall literature in Pattern recognition.
His studies deal with areas such as High dimensionality and Nonlinear regression as well as Data mining.
His work on Decision tree as part of general Machine learning research is frequently linked to Decision table and Medical diagnosis, bridging the gap between disciplines.
His most cited work include:
- Chi2: feature selection and discretization of numeric attributes (681 citations)
- A probabilistic approach to feature selection - a filter solution (581 citations)
- Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation (391 citations)
What are the main themes of his work throughout his whole career to date?
Rudy Setiono mostly deals with Artificial intelligence, Artificial neural network, Data mining, Machine learning and Pattern recognition.
His Artificial intelligence study frequently draws connections between adjacent fields such as Discretization.
His Artificial neural network study incorporates themes from Decision tree and Pruning.
The study incorporates disciplines such as Domain and Decision rule in addition to Data mining.
His research integrates issues of Knowledge acquisition and Knowledge-based systems in his study of Machine learning.
His work in the fields of Training set, Classifier and Extraction algorithm overlaps with other areas such as Hyperplane and Breast cancer.
He most often published in these fields:
- Artificial intelligence (68.91%)
- Artificial neural network (66.39%)
- Data mining (40.34%)
What were the highlights of his more recent work (between 2006-2021)?
- Artificial neural network (66.39%)
- Artificial intelligence (68.91%)
- Data mining (40.34%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial neural network, Artificial intelligence, Data mining, Decision tree and Machine learning.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Support vector machine and Cluster analysis.
Rudy Setiono has included themes like Discretization and Pattern recognition in his Artificial intelligence study.
The concepts of his Data mining study are interwoven with issues in Segmentation, Feedforward neural network, Training set and Feature selection.
His study looks at the relationship between Feature selection and fields such as Data science, as well as how they intersect with chemical problems.
His Machine learning research is multidisciplinary, relying on both Prediction algorithms and Knowledge-based systems.
Between 2006 and 2021, his most popular works were:
- Feature Selection: An Ever Evolving Frontier in Data Mining (235 citations)
- Recursive Neural Network Rule Extraction for Data With Mixed Attributes (109 citations)
- Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (67 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His scientific interests lie mostly in Artificial intelligence, Artificial neural network, Machine learning, Data mining and Context.
His work on Artificial intelligence deals in particular with Knowledge extraction, Data set and Decision tree.
His Decision tree study combines topics from a wide range of disciplines, such as Disjoint sets, Algorithm design and Statistical classification.
Rudy Setiono specializes in Machine learning, namely Support vector machine.
His biological study spans a wide range of topics, including High dimensionality and Feedforward neural network.
Rudy Setiono combines subjects such as Time delay neural network, Pattern recognition and Pruning with his study of Feedforward neural network.
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