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Topic: Naive Bayes classifier

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	Naive Bayes classifier - Wikipedia, the free encyclopedia
		A naive Bayes classifier (also known as Idiot's Bayes) is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions.
		The naive Bayes classifier combines this model with a decision rule.
		Hierarchical Naive Bayes Classifiers for uncertain data (an extension of the Naive Bayes classifier).
	en.wikipedia.org /wiki/Naive_Bayes_classifier (1308 words)

	BioMed Central \| Full text \| Predicting DNA-binding sites of proteins from amino acid sequence
		We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input.
		Table 1 shows that the classifier using amino acid identities as input achieved an overall accuracy of 71% with a correlation coefficient of 0.24, 35% of the residues predicted to be interface residues are actually interface residues, and 53% of interface residues are correctly identified.
		Classifiers trained using features other than the amino acid identities of target residue and its neighbors as input achieved performance that was lower than that of the classifier using amino acid identities of the corresponding residues as input (data not shown).
	www.biomedcentral.com /1471-2105/7/262 (6078 words)

	Naive Bayes Classifier
		The Naive Bayes Classifier technique is based on the so-called Bayesian theorem and is particularly suited when the dimensionality of the inputs is high.
		Our task is to classify new cases as they arrive, i.e., decide to which class label they belong, based on the currently exiting objects.
		In effect, Naive Bayes reduces a high-dimensional density estimation task to a one-dimensional kernel density estimation.
	www.statsoft.com /textbook/stnaiveb.html (723 words)

	Naive Bayes classifier (Site not responding. Last check: 2007-11-04)
		Naive Bayes classifiers are based on probability models that incorporate strong independence assumptions which often have no bearing in reality, hence are (deliberately) naive.
		In spite of their naive design and apparently over-simplified assumptions, naive Bayes classifiers often work much better in many complex real-world situations than might be expected from their very simple design.
		The naive Bayes classifier has several properties that make it surprisingly useful in practice, despite the fact that the far-reaching independence assumptions are often violated.
	naive-bayes-classifier.iqnaut.net (1150 words)

	There has recently been a lot of research in combining labeled
		The first part is the implementation of the Naive Bayes classifier, test and analysis on the test results of the accuracy rate under different conditions, such as with/out high frequency words, with/out low frequency words, different parameter values to treat the words that do not appear in the training set.
		Naive Bayes classifier is one the best performance text classifiers, it is widely used in text classification.
		In this project, Naïve Bayes classifier and the EM algorithm are experimented under different conditions.
	www.cs.cmu.edu /afs/cs/user/pzheng/www/ive/Text_classifier.htm (1732 words)

	[No title]
		The naive Bayes assumption in the first case implies that the presence or absence of a word is independent of that of other words, while in the second case, it implies that each word event is independent of other word events in the document.
		The naives Bayes rule (in this context) is that, the occurence of a particular word type in a document is independent of the occurence of other word types in the same document and hence, their individual probabilities get multiplied.
		The naive Bayes assumption made here is that the probability of a word occuring in the generated document is independent of the occurence of other words in the document.
	www.cs.wisc.edu /~apirak/cs/cs838/reviews_score_1.html (4802 words)

	BioMed Central \| Full text \| Mining housekeeping genes with a Naive Bayes classifier
		For the actual classification two different Naive Bayes algorithms were tested, a classic version and the AODE (Averaged One-Dependence Estimators) version [20,21] that should reduce the error due to non-independent attributes.
		As expected, the Naive Bayes classifier shows a definite progression in performance when data discretisation is used, either supervised or un-supervised with frequency binning, as shown in Figure 2 for human data.
		The Naive Bayes algorithm is also extremely fast compared to the others tested: there is no significant wait time for a 10-times 10-fold cross-evaluation of ≈ 600 transcripts training set, and it takes only a couple of seconds to obtain the classification of a 40000 instances test set (data not shown).
	www.biomedcentral.com /1471-2164/7/277 (6577 words)

	Synaptic » Blog Archive » Classifier Showdown
		A classifier is a system that performs a mapping from a feature space X to a set of labels Y. Basically what a classifier does is assign a pre-defined class label to a sample.
		In this case the Bayes classifier is the clear winner due to its speed and simplicity.
		The Naive Bayes classifier is simple, fast and of limited capability when it comes to anything but the most trivial cases of classification.
	blog.peltarion.com /2006/07/10/classifier-showdown (2202 words)

	Machine Learning: Naive Bayes Classifier
		Naive Bayes classifiers are among the more successful known algorithms for classifying text based documents.
		In some domains, the performance of a Naive Bayes learner is comparable to that of neural network and decision tree learning.
		The most distinctive feature of Naive Bayes classifier is that it has no explicit search through the space of possible hypotheses.
	www.cs.columbia.edu /~evs/ml/MLfinalproj/chun/report.html (1469 words)

	Automatic Document Classification With Perl
		One common approach is to use the ``Naive Bayes'' classifier, which involves a little basic probability and very little guidance from a human director.
		It should be noted, however, that the training data is not completely representative of the test data, because documents in the archive have been selected for their quality and clarity, and they have been edited for grammar.
		Before describing the classifier itself, it is necessary to cover the basic probability behind its operation.
	mathforum.org /~ken/bayes/bayes.html (992 words)

	Naive Bayes
		The naive Bayesian classifier is known to be optimal when attributes are independent given the class.
		In this project we show that, although the Bayesian classifier's probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin.
		The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality.
	www.cs.washington.edu /ai/naive.html (221 words)

	Implement Bayesian inference using PHP: Part 3
		The final paragraph of this quote is interesting because it suggests that the goal of data analysis in classifier-oriented research should be to construct a classifier that is, first of all, accurate and, after that, one that is the simplest in terms of the number of variables that are used by the classifier.
		The Naive Bayes classifier assumes that the responses to each test question are conditionally independent of each other.
		I discussed the fact that the Naive Bayes classification formula makes a strong independence assumption between classification attributes, but that even when this assumption is violated, a Naive Bayes classifier often works quite well.
	www-128.ibm.com /developerworks/web/library/wa-bayes3 (4034 words)

	Naive Bayes-Introduction
		Naive Bayes classification gets around this problem by not requiring that you have lots of observations for each possible combination of the variables.
		In other words, Naïve Bayes classifiers assume that the effect of a variable value on a given class is independent of the values of other variable.
		Bayes theorem is useful in that it provides a way of calculating the posterior probability, P(HX), from P(H), P(X), and P(XH).
	www.resample.com /xlminer/help/NaiveBC/classiNB_intro.htm (332 words)

	5. Algorithms
		The naive Bayes classifier computes the likelihood that a program is malicious given the features that are contained in the program.
		The votes were combined by the Multi-Naive Bayes algorithm to output a final classification for all the Naive Bayes.
		Each classifier gives a probability of a class C given a set of bytes Fwhich the Multi-Naive Bayes uses to generate a probability for class C given F over all the classifiers.
	www.fsl.cs.sunysb.edu /docs/binaryeval/node5.html (1523 words)

	[No title]
		Automated Discovery of Sequence Motif Based Protein Function Classifiers Using Reduced Alphabet Protein Sequence Representations In this study, we explore the use of alternative protein sequence representations for data-driven discovery of a hypothesis representation, in the form of Naïve Bayes classification, to assigning proteins to functional families.
		We seek a classifier that can be used to reliably assign novel protein sequences to one of the protein families represented in the training set based on the motif composition of the sequences.
		Because Naïve Bayes classifiers do not need to be constructed from aligned sequences, we used this method to speed up the process of building classifiers and updating these classifiers.
	www.cs.iastate.edu /~andorfc/papers/Naivepaper.doc (4177 words)

	BioJava:CookBook:Distribution:Bayes - BioJava (Site not responding. Last check: 2007-11-04)
		Naive bayes classifiers are one of the simplist examples of probabilistic classifiers.
		Most commonly they are used for supervised learning and classify observations based on maximum likelihood.
		The application is somewhat similar to a weight matrix with a non-uniform null (background) distribution except that an entire sequence is classified not subsequences as would be the case with a weight matrix.
	www.biojava.org /wiki/BioJava:CookBook:Distribution:Bayes (719 words)

	3.8 Naive Bayes
		Naive Bayes is a rule generator based on Bayes's rule of conditional probability.
		Here is a sample execution of the rule generator with gamma ray burst data taken from the MFBMFR 3B catalog, using the attributes Log T90, Log HR321, and Log Fluence as input attributes and the attribute MFBMFR Class as the output attribute.
		Treats variable as independent and equally important, which can cause skewed results, especially if many of the variables are interrelated, as that relation will have a greater effect on the decision, for better or for worse.
	grb.mnsu.edu /grbts/doc/manual/Naive_Bayes.html (688 words)

	Data Mining Technique - Bayesian Approaches
		Bayes Rule is applied here to calculate the posterior from the prior and the likelihood, because the later two is generally easier to be calculated from a probability model.
		The Naïve Bayes Classifier technique is particularly suited when the dimensionality of the inputs is high.
		Bayes Net is a model of utilizing the conditional probabilities among different variables.
	www.cs.queensu.ca /home/xiao/dm.html (2427 words)

	Programming Assignment 4
		For this programming assignment, we want you to implement a Naive Bayes Classifier and use it to predict whether an individual is likely to play tennis given a set of weather conditions.
		However, the Naive Bayes Classifier relies on the simplifying assumption that all the input features are conditionally independent of given the desired output value, which in this case is the value of Play_Tennis.
		After the probabilities have been calculated, Naive Bayes can be used to predict the class of a new instance by calculating which class is most likely given the input.
	www-anw.cs.umass.edu /~cs383/program4/program4.html (1067 words)

	[No title] (Site not responding. Last check: 2007-11-04)
		Please check there for announcements, etc. For this exercise we will be using both the Naive Bayes classifier and and the C4.5 decision tree classifier used in exercise 1.
		Invoke the Naive Bayes classifier and then the J48 classifer using zoo.train.arff and zoo.test.arff as the training and testing files respectively.
		Invoke the classifier on the zoo.train.arff and zoo.test.arff files as before, but setting the minimum number of instances allowed to 1 (the default is 2).
	odur.let.rug.nl /~james/MLcourse/assignment2 (444 words)

	nbc:: A naive bayes classifier
		However, in the specific case of a naive bayes classifier for discrete data, it is interesting to test if less is indeed more.
		Not surprisingly, nbc.awk's accuracy are very similar to a standard bayes classifier (from the WEKA system).
		Nbc.awk does better than WEKA Bayes on the datasets shown at the bottom of the table.
	web.cecs.pdx.edu /~timm/dm/nbc.html (1072 words)

	MACHINE LEARNING
		Recall that the Naive Bayes Classifier assumes that the attributes are independent given the class label.
		All the classifier classes are passed to the Evaluation class.
		You should plan on turning in a report comparing the performance of the Standard Naive Bayes Classifier with the classifier generated by either Friedman's algorithm or Zhang's algorithm (depending on which one of the two you chose to implement) on each data set.
	www.cs.iastate.edu /~cs573x/labs/lab2/04lab2.html (963 words)

	Using Naive Bayes algorithm To Classify Text
		How to classify documents, that is, how to assign documents to a class, and how precise it is, still remains as one of the current research topics.
		Naive Bayes algorithm can be used very successfully in text categorization task.
		The naive Bayes classifier, which is using highly practical Bayesian learning method, is said to be quite successful when applied to learning tasks to classify natural language text documants, and in some domains its performance has been shown to be compatible to that of neural network and decision tree learning.
	www.cs.columbia.edu /~evs/ml/OthelloStudProj/huang/write-up.html (2299 words)

	Full and Naive Bayes Classifiers (Site not responding. Last check: 2007-11-04)
		To induce a Bayes classifier for the effective drug, one first has to determine the domains of the table columns using the program
		But occasions may arise in which you want to induce a naive Bayes classifier from a subset of the columns or in which the numbers in a column are actually coded symbolic values.
		If the table contains preclassified data and the name of the column containing the preclassification is the same as for the training data, the error rate of the naive Bayes classifier is determined and printed to the terminal.
	fuzzy.cs.uni-magdeburg.de /~borgelt/doc/bayes/bayes.html (1282 words)

	AiLab.si (Site not responding. Last check: 2007-11-04)
		Orange includes a component based naive Bayesian classifier that can handle both, discrete and continuous attributes, while the class needs to be discrete (or, at least discretized).
		Default is false (to conform with the usual naive bayesian classifiers), but setting it to true can increase the accuracy considerably.
		The classifier is correct in all five cases.
	www.ailab.si /orange/doc/reference/BayesLearner.htm (1202 words)

	[Abstract] A Robust Watermarking Method for Color Images using Naive-Bayes Classifier
		By watermarking an image, we hide a pattern in it in such a way that the pattern is not visible but can be extracted using a decomposition algorithm and a key in the receiver side.
		In this paper, we present a watermarking method for color images which is robust with respect to usual attacks such as noise addition, smoothing, compression and also rotation.
		The validity of correctness of retrieved water mark is based on the result of a Naive-Bayes classifier.
	www.actapress.com /Abstract.aspx?paperId=15656 (214 words)

	Using a mixture of trees as a classifier
		A density estimator can be turned into a classifier in two ways, both of them being essentially likelihood ratio methods.
		In particular, if the trees are forced to have the same structure we obtain the Tree Augmented Naive Bayes (TANB) classifier of [Friedman, Geiger, Goldszmidt 1997].
		The analog of the MF classifier in this setting is the naive Bayes classifier.
	www.ai.mit.edu /projects/jmlr/papers/volume1/meila00a/html/node28.html (195 words)

	PubMed Wizard - BMC (Site not responding. Last check: 2007-11-04)
		Results: We propose an extension of the well-known Naive Bayes classifier, which accounts for biological heterogeneity in a probabilistic framework, relying on Bayesian hierarchical models.
		Conclusions: The proposed Hierarchical Naive Bayes classifier can be conveniently applied in problems where within sample heterogeneity must be taken into account, such as TMA experiments and biological contexts where several measurements (replicates) are available for the same biological sample.
		The performance of the new approach is better than the standard Naive Bayes model, in particular when the within sample heterogeneity is different in the different classes.
	www.biowizard.com /BMC_Bioinfo_Def.aspx (2892 words)

	[No title] (Site not responding. Last check: 2007-11-04)
		The algorithm consists of building a classifier using a very small set of previously labeled data, then classifying a larger set of unlabeled data using that classifier, and finally building a new classifier using a combined data set containing the original set of labeled data and the set of previously unlabeled data.
		Basically, the co-training algorithm is this: two weak classifiers are built, each one using different kind of information, then, bootstrap from these classifiers using unlabeled data.
		2.2 Naive Bayes Classifier The Naive Bayes classifier is a probabilistic algorithm based on the simplifying assumption that the attribute values are conditionally independent given the target values.
	cseg.inaoep.mx /~fuentes/solorio_fuentes2.doc (2615 words)

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