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Full-Text Articles in Electrical and Computer Engineering
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …
Comparative Analysis Of Feature Selection Methods To Identify Biomarkers In A Stroke-Related Dataset, Thomas Clifford, Justin Bruce, Tayo Obafemi-Ajayi, John Matta
Comparative Analysis Of Feature Selection Methods To Identify Biomarkers In A Stroke-Related Dataset, Thomas Clifford, Justin Bruce, Tayo Obafemi-Ajayi, John Matta
Electrical and Computer Engineering Faculty Research & Creative Works
This paper applies machine learning feature selection techniques to the REGARDS stroke-related dataset to identify health-related biomarkers. A data-driven methodological framework is presented to evaluate multiple feature selection methods. In applying the framework, three classifiers are chosen in conjunction with two wrappers, and their performance with diverse classification targets such as Current Smoker, Current Alcohol Use, and Deceased is evaluated. The performance across logistic regression, random forest and naïve Bayes classifier methods, as quantified by the ROC Area Under Curve metric and selected features, was similar. However, significant differences were observed in running time. Performance of the selected features was …
Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch
Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
We applied an architecture which automates the design of simultaneous recurrent network (SRN) using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the simultaneous recurrent network for the engine data classification. The experimental results show that our approach gives …
Landmine Detection And Discrimination Using High-Pressure Waterjets, Daryl G. Beetner, R. Joe Stanley, Sanjeev Agarwal, Deepak R. Somasundaram, Kopal Nema, Bhargav Mantha
Landmine Detection And Discrimination Using High-Pressure Waterjets, Daryl G. Beetner, R. Joe Stanley, Sanjeev Agarwal, Deepak R. Somasundaram, Kopal Nema, Bhargav Mantha
Electrical and Computer Engineering Faculty Research & Creative Works
Methods of locating and identifying buried landmines using high-pressure waterjets were investigated. Methods were based on the sound produced when the waterjet strikes a buried object. Three classification techniques were studied, based on temporal, spectral, and a combination of temporal and spectral approaches using weighted density distribution functions, a maximum likelihood approach, and hidden Markov models, respectively. Methods were tested with laboratory data from low-metal content simulants and with field data from inert real landmines. Results show that the sound made when the waterjet hit a buried object could be classified with a 90% detection rate and an 18% false …
Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Electrical and Computer Engineering Faculty Research & Creative Works
Strategies to efficiently train a neural network for an aerospace problem with a large multidimensional input space are developed and demonstrated. The neural network provides classification for over 100,000,000 data points. A query-based strategy is used that initiates training using a small input set, and then augments the set in multiple stages to include important data around the network decision boundary. Neural network inversion and oracle query are used to generate the additional data, jitter is added to the query data to improve the results, and an extended Kalman filter algorithm is used for training. A causality index is discussed …