Modeling Unreliable Data and Sensors Using Event Log Performance and F-Measure Attribute Selection

• Published in: Intelligent Sensor Networks pp. 31 - 53
• Main Authors: Iyer, Vasanth, Iyengar, S. Sitharama, Pissinou, Niki
• Format: Book Chapter
• Language: English
• Published: CRC Press 2013
• Edition: 1
• Subjects: Bayes Net; Small Fires; Sensor Networks; Major Fire Events; Forest Fire Event; SVM Classifier; Wireless Sensor Networks; Attribute Selection; Bell Shape Curve; Fire Events; Fire Activity; Bayes Network; Ranking Function; Distributed Kalman Filtering; Training Samples Collection; Burnt Area; Sensor Measurements; Cognitive Wireless Sensor Networks; Intelligent Sensor Networks; FWI; Gaussian Density Function; Weather Data; Large Fires
• ISBN: 1138199745; 9781439892817; 1439892814; 9781138199743
• DOI: 10.1201/b14300-3

The baseline discrete parameters capture only the sensor ranges, making event prediction function hard to train with a Gaussian density function, without specific temporal understanding of the datasets. The dynamic features present in a sequence of patterns are localized and used to predict events, which otherwise may be an attributing feature to the static data mining algorithm. The machine learning repository provides collection of supervised databases that are used for the empirical analysis of event prediction algorithms with unsupervised datasets from distributed wireless sensor networks. A measure which combines precision and recall for a small dataset is F-measure and is the weighted harmonic mean of precision and relevance. The availability of such a system is expected to allow more flexible modeling approaches and much more rapid model turnaround for exploratory analysis. From statistical point of view, if the attributes have similar values then it creates high bias creating what is called over-fitting error during learning.