Positive Unlabeled Learning

Kristen Jaskie received her Ph.D. in Signal Processing and Machine Learning through the Electrical Engineering department and the SenSIP center at Arizona State University in Tempe, Arizona in 2021 and her B.S. and M.S. degrees in Computer Science with an emphasis in Machine Learning from the University of Washington in Seattle, Washington and the University of California San Diego in San Diego, California, respectively. Kristen is the principal ML research scientist at Prime Solutions Group and a postdoctoral researcher at ASU working with Dr. Spanias in the SenSIP center. Kristen’s research interests include machine learning and deep learning algorithm development and application, with a focus on semi-supervised learning and the positive and unlabeled learning problem. She is the author of multiple papers including “Positive and Unlabeled Learning Algorithms and Applications: a Survey,” “A Modified Logistic Regression for Positive and Unlabeled Learning,” and “PV Fault Detection Using Positive Unlabeled Learning.” Andreas Spanias is a Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (also an NSF I/UCRC site). His research interests are in the areas of adaptive signal processing, speech processing, machine learning, and sensor systems. He and his student team developed the computer simulation software Java-DSP and its award-winning iPhone/iPad and Android versions. He is the author of two textbooks: Audio Processing and Coding by Wiley and DSP–An Interactive Approach (2nd Ed.). He contributed to more than 300 papers, 11 monographs, 13 full patents, 6 provisional patents, and 10 patent pre-disclosures. He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished Lecturer for the IEEE Signal Processing society in 2004. He is a series editor for the Morgan and Claypool lecture series on algorithms and software. He recently received the 2018 IEEE Phoenix Chapter award with citation: “For significant innovations and patents in signal processing for sensor systems.” He also received the 2018 IEEE Region 6 Educator Award (across 12 states) with citation: “For outstanding research and education contributions in signal processing.” He was elected recently as Senior Member of the National Academy of Inventors (NAI).

• Preface.- Acknowledgments.- Introduction.- Problem Definition.- Evaluating the Positive Unlabeled Learning Problem.- Solving the PU Learning Problem.- Applications.- Summary.- Bibliography.- Authors' Biographies.