This paper has been carried out to make a performance evaluation of classification using Various Data Mining algorithm. The paper sets out to make comparative evaluation of classifiers in the context of dataset of Indian news to maximize true positive rate and minimize false positive rate. For ...
Discriminant Measures for Classification Performance play a critical role in guiding the design of classifiers, assessment methods and evaluation measures are at least as important as algorithm and are the first key stage to a successful data mining. We systematically summarized the evaluation measures ...
Gomathi.V.V and Hemalatha.M, “Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set,” Bonfring Int - Devasena, Sumathi () Citation Context ...e classification of Multivariate Datasets without Missing Values is investigated in [1]. The effectiveness ...
Comparative Analysis of Data Mining Classifiers in Analyzing Clinical Data Health-care providers know there a wealth of valuable information trapped in the hand-written notes on patients charts. But the challenge of collecting and interpreting the data on a large scale remains to be solved. Now, ...
Real-world datasets are not perfect and always suffer from noise that may affect classifiers built under the effect of such type of disturbance. Different types of noise are existing in almost any real-world problem, but not always known. Existence of noise decreases the accuracy of a classifier...
We consider two type of classifiers: support vector machines (SVM) and k-nearest neighbour (kNN). Results of leave-one-out cross validation (LOOCV) ... E Marchiori,NHH Heegaard,CR Jimenez,... - IEEE Symposium on Computational Intelligence in Bioinformatics & Computational Biology 被引量: 26发...
In some cases, researchers concluded differently: For example, in the case of size metrics, Gyimothy et al. reported good results (2005), as opposed to the findings of Fenton and Ohlsson (2000). Various types of classifiers have been applied to this task, including statistical procedures (...
In evaluation models for intangible assets, the results provide that DT + DT (i.e. classifier + classifier in hybrid classifiers) performs the best of others in terms of prediction accuracy, Type I and II errors and ROC curve. Specifically, while the best single classifier, DT provides 75.78...
Another fundamental issue that is not sufficiently considered is the sensitivity of classifiers both to class imbalance as well as to having only a small number of samples of the minority class. We consider such questions in this paper.doi:10.1007/978-3-642-23166-7_12Troy Raeder...
An Empirical evaluation of CostBoost Extensions for Cost-Sensitive Classification Ensemble learning, combining multiple classifiers using bagging, boosting or stacking, are proven data-mining methods, we have used boosting in this paper ... A Desai,K Jadav,S Chaudhary - ACM 被引量: 0发表: 2015年...