[PDF] Top 20 Bayesian classification for data from the same unknown class
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Bayesian classification for data from the same unknown class
... ONCLUSIONS The naive Bayes classifier is widely used in many clas- sification tasks because its performance is competitive with state-of-the-art classifiers, it is simple to implement, and it possesses fast ... See full document
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Bayesian Classication for Set and Interval Data
... naive Bayesian classi er to have the abilities of process- ing interval-valued ...have the same unknown class label. The key of our approach is based on our study on ... See full document
8
BAYESIAN INFERENCES OF LATENT CLASS MODELS WITH AN UNKNOWN NUMBER OF CLASSES
... Procedure For identifiability concerns, we have adopted a unique labeling for class membership in each RJMCMC ...of the on-line processing method originally developed by Celeux, Hurn, and ... See full document
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A new method for generating fuzzy rules from numerical data for handling classification problems
... METHOD FOR FUZZY RULES GENERATION In the following, we brie¯ y review Yuan and Shaw’s method for fuzzy rules generation ...called the object space (Yuan & Shaw, ...is the domain of ... See full document
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Cascaded Class Reduction for Time-Efficient Multi-Class Classification
... cascaded class re- duction to combine a sequence of classifiers that suc- cessively reduces the set of possible ...long classification time. Experiments on data sets collected from new ... See full document
6
Breast Cancer Classification and Biomarker Discovery on Microarray Data Using Genetic Algorithms and Bayesian Classifier
... adopting Bayesian classifier to solve multi-class classification problem on microarray data of breast ...cancers. The experimental results prove the effectiveness and superiority ... See full document
6
Fuzzy classification trees for data analysis
... particular, the boundaries among classes are not always clearly de!ned. For example, there are usually uncertainties in diagnoses based on data from biochemical laboratory ...make the ... See full document
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Probability Estimates for Multi-class Classification by Pairwise Coupling
... as the Binary Classifier In this subsection we consider random forest (Breiman, 2001) as the binary classifier and conduct experiments on the same data ...denote the ... See full document
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Probability Estimates for Multi-class Classification by Pairwise Coupling
... to the randomness of separating training data into five folds for finding the best (C, γ), we repeat the five-fold cross-validation five times and obtain the mean and standard ... See full document
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Class-imbalanced classifiers for high-dimensional data
... suffer from lack of data; each factor or combination affects particular classifiers differ- ...combination. For the algorithms and correction strategies investigated in this study, DLDA ... See full document
14
A DIAMOND method of inducing classification rules for biological data
... when the boundaries between the classes are ...rules from datasets containing non-linear interactions between the input data and the classes to be ...separates the objects ... See full document
13
Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems
... investigated the mechanism of carcino- genesis by analyzing the gene expression profiles from microarray ...important for proper treatment and prognosis. The application of gene ... See full document
33
Machine learning with automatic feature selection for multi-class protein fold classification
... [1], the authors used proteins from the PDB where two proteins had no more than 35% of sequence ...to the three amino acid attributes described earlier, the authors used three more ... See full document
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A NOVEL LEARNINGALGORITHM FOR DATA CLASSIFICATION WITH RADIAL BASIS FUNCTION NETWORKS
... algorithm for constructing data classifiers with radial basis function (RBF) ...networks. The RBF networks constructed with the proposed learning algorithm generally are able to deliver ... See full document
6
Breast cancer classification and biomarker discovery from microarray data using silhouette statistics and genetic algorithms
... that the classification rule can also be used to predict the labels of novel ...samples. For a novel sample, its label should be assumed to be from C 1 to C q and the ... See full document
6
Feature Enrichment Based Convolutional Neural Network for Heartbeat Classification From
... observations from Table 3 are as follows. First, the values of Sen and Ppr for V beat detection are higher than those for S beat ...in the training set due to the fact that ... See full document
10
CUSTOMER SEGMENTATION AND CLASSIFICATION FROM BLOGS BY USING DATA MINING: AN EXAMPLE OF VOIP PHONE
... describe the collected instances. In step 3, some data preprocess techniques such as data cleaning, data normalization, and frequency analysis are applied to the collected ...because ... See full document
27
Generating Weighted Fuzzy Rules From Training Data for Handling Fuzzy Classification Problems
... rules from a set of training data to deal with the Saturday Morning Problem [17], where the attributes appearing in the antecedent parts of the generated fuzzy rule may have ... See full document
8
Support vector machines for data classification
... • Engine misfire: a substantial fraction of a cylinder’s air-fuel mixture fails to ignite.. • Frequent misfires: pollutants and costly replacement[r] ... See full document
26
A new robust model reference control for a class of multivariable unknown plants
... With moderate unmodeled dynamics and bounded output disturbances, the controller, which combines the characteristics of variable structure design [14] [15] and null adapt[r] ... See full document
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