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Accession Number ADA580069
Title Kernel Extended Real-Valued Negative Selection Algorithm (KERNSA).
Publication Date Jun 2013
Media Count 87p
Personal Author B. A. Smith
Abstract Artificial Immune Systems (AISs) are a type of statistical Machine Learning (ML) algorithm based on the Biological Immune System (BIS) applied to classification problems. Inspired by increased performance in other ML algorithms when combined with kernel methods, this research explores using kernel methods as the distance measure for a specific AIS algorithm, the Real- valued Negative Selection Algorithm (RNSA). This research also demonstrates that the hard binary decision from the traditional RNSA can be relaxed to a continuous output, while maintaining the ability to map back to the original RNSA decision boundary if necessary. Continuous output is used in this research to generate Receiver Operating Characteristic (ROC) curves and calculate Area Under Curves (AUCs), but can also be used as a basis of classification confidence or probability. The resulting Kernel Extended Real-valued Negative Selection Algorithm (KERNSA) offers performance improvements over a comparable RNSA implementation. Using the Sigmoid kernel in KERNSA seems particularly well suited (in terms of performance) to four out of the eighteen domains tested.
Keywords Accuracy
Algorithms
Anomalies
Anomaly detection
Artificial immune systems
Artificial intelligence
Classification
Clustering
Detection
Diabetes dataset
Experimental data
Ionosphere dataset
Iris dataset
Kernel extended real-valued negative selection algorithms
Kernel functions
Kernel methods
Learning machines
Parameters
Probability
Sonar dataset
Statistical analysis
Statistical machine learning
Theses
Training
Wine dataset
Wisconsin diagnostic breast cancer dataset


 
Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
72F - Statistical Analysis
62 - Computers, Control & Information Theory
Corporate Author Air Force Inst. of Tech., Wright-Patterson AFB, OH. Graduate School of Engineering and Management.
Document Type Thesis
Title Note Master's thesis.
NTIS Issue Number 1325
Contract Number N/A

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