Idiot's Bayes—Not So Stupid After All?

Hand, David J.; Yu, Keming

doi:10.1111/j.1751-5823.2001.tb00465.x

Cited by 495 publications

(231 citation statements)

References 53 publications

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“…Although perhaps not recognised when the approach was first introduced, substructural analysis is an example of a naive Bayesian classifier (NBC) [17,63,64]. An NBC is a simple classification algorithm that is based on the use of Bayes' theorem and on strong assumptions as to the statistical independence of the descriptors characterising the objects that are to be classified.…”

Section: Substructural Analysis Naive Bayesian Classifiers and Groupmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

115

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Abstract. Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of a naive Bayesian classifier when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

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Section: Substructural Analysis Naive Bayesian Classifiers and Groupmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

115

View full text Add to dashboard Cite

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“…While this does not allow an exact membership to be determined, obtaining approximate probabilities while circumventing the curse of dimensionality as well as coping with several missing values makes this a powerful method. See also [15,16].…”

Section: Methodology Bayesian Event Classificationmentioning

confidence: 99%

Towards Real-time Classification of Astronomical Transients

Mahabal¹,

Djorgovski²,

Williams³

et al. 2008

AIP Conference Proceedings

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Abstract. Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are needed; for short-lived phenomena, a rapid response is essential. Ability to automatically classify and prioritize transient events for follow-up studies becomes critical as the data rates increase. We have been developing such methods using the data streams from the Palomar-Quest survey, the Catalina Sky Survey and others, using the VOEventNet framework. The goal is to automatically classify transient events, using the new measurements, combined with archival data (previous and multi-wavelength measurements), and contextual information (e.g.. Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.); and to iterate them dynamically as the follow-up data come in (e.g., light curves or colors). We have been investigating Bayesian methodologies for classification, as well as discriminated follow-up to optimize the use of available resources, including Naive Bayesian approach, and the non-parametric Gaussian process regression. We will also be deploying variants of the traditional machine learning techniques such as Neural Nets and Support Vector Machines on datasets of reliably classified transients as they build up.

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“…Naïve Bayes is regarded as the modest brand of Bayesian network in which all properties can beself-regulating and are straightforwardlyassociatedwith labels. This is known as conditional independence (Hand and Yu, 2001;Zhang, 2004;Rish, 2001).…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

“…Based on the rule of Bayes, the probability of E = (x 1 , x 2 ,…,x n ) included in labelc can be expressed via (1) (Hand and Yu, 2001;Zhang, 2004;Rish, 2001):…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

“…Note that p(Ec) is the probability of a selected exampleE included in a label c, p(Ec) is probability of generating a selected instance included in E, given the label c, p(c) is probability of occurrence of the label c and p(E) is probability of a selected example included in E. note that the example E is categorized as C which is a positive label (+) (Hand and Yu, 2001;Zhang, 2004;Rish, 2001):…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

See 1 more Smart Citation

Lecturer Performance Analysis using Multiple Classifiers

Ahmad¹,

Rashid²

2016

Journal of Computer Science

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Lecturer performance analysis has enormous influence on the educational life of lecturers in universities. The existing system in universities in Kurdistan-Iraq is conducted conventionally, what is more, the evaluation process of performance analysis of lecturers is assessed by the managers at various branches at the university andin view of that, in some cases the outcomes of this process cause a low level of endorsement among staffs who believe that most of these cases are opinionated. This paper suggests a smart and an activesystem in which both unique and multiple soft computing classifier techniques are used to examine performance analysis of lecturers of college of engineering at Salahaddin University-Erbil (SUE). The dataset collected from the quality assurancedepartment at SUE. The dataset composes of three sub-datasets namely: Student Feedback (FB), Continuous Academic Development (CAD) and lecturer's portfolio (PRF). Each of the mentioned subdatasets is classified with a different classifier technique. FB uses BackPropagation Neural Network (BPNN), CAD uses Naïve Bayes Classifier (NBC) and the third sub-dataset uses Support Vector Machine (SVM) as a classifier technique. After implementing the system, the results of the above sub-datasets are collected and then fed as input data to BPNN technique to obtain the final result and accordingly, the lectures are awarded, warned or punished.

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Idiot's Bayes—Not So Stupid After All?

Cited by 495 publications

References 53 publications

Evaluation of machine-learning methods for ligand-based virtual screening

Evaluation of machine-learning methods for ligand-based virtual screening

Towards Real-time Classification of Astronomical Transients

Lecturer Performance Analysis using Multiple Classifiers

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