Summary In this paper, we analysed classification rules under Bayesian decision theory. The setup we considered here is fairly general, which can represent all possible parametric models. The Bayes classification rule we investigated minimises the Bayes risk under general loss functions. Among the ...
1 Introduction Although Bayesian methods have been studied for many years, it is only recently that their practical application has become truly widespread. This is due in large part to the relatively high computational overhead of performing the marginalizations (integrations and summa- tions) which...
Quantum Chromodynamics: the traditional one based on renormalisation and factorisation scale variation, and the Bayesian framework proposed by Cacciari and Houde... E Bagnaschi,M Cacciari,A Guffanti,... - 《Journal of High Energy Physics》 被引量: 20发表: 2014年 Imbalanced TSK Fuzzy Classifier ...
Bayesian rule learning for biomedical data mining Motivation: Disease state prediction from biomarker profiling studies is an important problem because more accurate classification models will potentially ... GF Cooper - 《Bioinformatics》 被引量: 68发表: 2010年 Learning classification rules from data* ...
Theclassification schemeis a hierarchical grouping and ordering of approx. 1300 keywords into a total of five levels (more specifically, one top-level with a maximum of four sub-levels). In this section we describe the rationale for selecting the top-level keywords and provide further details on...
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999) Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting [Paper] The Alternating Decision Tree Learning Algorithm (ICML 1999) Yoav Freund, Llew Mason [Paper] [Code] Boosting with Multi-...
Random search (RS), meta-heuristic algorithm such as particle swarm optimization (PSO), GA, and Bayesian optimization (BO) were used as hyper-parameter optimization algorithms. To evaluate their framework, they used Canadian Institute of Cybersecurity’s IDS 2017 (CICIDS 2017) [56], and UNSW-...
We develop a novel knowledge-based Bayesian network (KBBN) that models our knowledge of the Mycobacterium tuberculosis complex (MTBC) obtained from expert-defined rules and large DNA fingerprint databases to classify strains of MTBC into fifty-one genetic sublineages. The model uses two high-through...
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In contrast, this paper can offer an effective and yet simple scoring scheme to measure the similarity between two rule sets. What is more, the scheme applies not only to classification rules, but also to other families of classifiers such as Bayesian networks that are commonly used in real-...