What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Data mining, Artificial intelligence, Software quality, Software metric and Machine learning are his primary areas of study.
The Data mining study combines topics in areas such as Sampling and Software, Software fault tolerance, Application software.
His work in Artificial intelligence covers topics such as Data modeling which are related to areas like Data set, Application domain, Software bug and Analysis of variance.
His research in Software quality intersects with topics in Software system, Software sizing, Software construction and Case-based reasoning.
The various areas that he examines in his Software metric study include Software measurement, Reliability, Reliability engineering and Software quality assurance.
As part of the same scientific family, Taghi M. Khoshgoftaar usually focuses on Machine learning, concentrating on Classifier and intersecting with Perceptron, Supervised learning, Radial basis function and Imbalanced data.
His most cited work include:
- RUSBoost: Improving classification performance when training data is skewed (125 citations)
- Evolutionary Optimization of Software Quality Modeling with Multiple Repositories (110 citations)
- Using regression trees to classify fault-prone software modules (106 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Data mining, Machine learning, Software quality and Software metric.
In his study, Stability and Noise measurement is inextricably linked to Pattern recognition, which falls within the broad field of Artificial intelligence.
His Data mining research includes themes of Sampling, Software and Filter.
His Machine learning study frequently draws connections between adjacent fields such as Data modeling.
He combines subjects such as Software system, Software sizing, Software construction and Reliability engineering with his study of Software quality.
His Software metric research integrates issues from Software measurement, Software fault tolerance, Software regression, Software development process and Genetic programming.
He most often published in these fields:
- Artificial intelligence (53.96%)
- Data mining (53.48%)
- Machine learning (39.57%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (53.96%)
- Machine learning (39.57%)
- Data mining (53.48%)
In recent papers he was focusing on the following fields of study:
Taghi M. Khoshgoftaar mainly focuses on Artificial intelligence, Machine learning, Data mining, Feature selection and Big data.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Pattern recognition.
His work carried out in the field of Machine learning brings together such families of science as Classifier, Robustness, Data modeling and Bioinformatics.
His study looks at the intersection of Data mining and topics like Boosting with Gradient boosting.
His Feature selection research is multidisciplinary, incorporating elements of Feature, Software, Undersampling and Software metric.
His Software metric study is within the categories of Software quality and Software development.
Between 2014 and 2021, his most popular works were:
- Big Data: Deep Learning for financial sentiment analysis (83 citations)
- Medicare fraud detection using neural networks (23 citations)
- A New Intrusion Detection Benchmarking System (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Taghi M. Khoshgoftaar mainly investigates Artificial intelligence, Big data, Data mining, Machine learning and Deep learning.
He carries out multidisciplinary research, doing studies in Artificial intelligence and Network level.
His studies deal with areas such as Data modeling, Class, Random forest, Data set and Pattern recognition as well as Big data.
His work on Intrusion detection system is typically connected to Actual/normal as part of general Data mining study, connecting several disciplines of science.
His Machine learning research is multidisciplinary, incorporating perspectives in Process, Null and Filter.
His Feature selection research includes elements of Task, Software quality, Software metric, Data reduction and Software.
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