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Full-Text Articles in Physical Sciences and Mathematics
Computational Intelligence Based Classifier Fusion Models For Biomedical Classification Applications, Xiujuan Chen
Computational Intelligence Based Classifier Fusion Models For Biomedical Classification Applications, Xiujuan Chen
Computer Science Dissertations
The generalization abilities of machine learning algorithms often depend on the algorithms’ initialization, parameter settings, training sets, or feature selections. For instance, SVM classifier performance largely relies on whether the selected kernel functions are suitable for real application data. To enhance the performance of individual classifiers, this dissertation proposes classifier fusion models using computational intelligence knowledge to combine different classifiers. The first fusion model called T1FFSVM combines multiple SVM classifiers through constructing a fuzzy logic system. T1FFSVM can be improved by tuning the fuzzy membership functions of linguistic variables using genetic algorithms. The improved model is called GFFSVM. To better …
Topic And Role Discovery In Social Networks With Experiments On Enron And Academic Email, Andrew Mccallum, Xuerui Wang, Andrés Corrada-Emmanuel
Topic And Role Discovery In Social Networks With Experiments On Enron And Academic Email, Andrew Mccallum, Xuerui Wang, Andrés Corrada-Emmanuel
Andrés Corrada-Emmanuel
Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient---steering the discovery of topics according to the relationships between people. We give results on both the Enron …