Now showing items 1-5 of 5
An Adaptive Ensemble Classifier for Mining Concept-Drifting Data Streams
(Expert Systems with Applications, 2013-11-01)
Traditional data mining techniques cannot be directly applied to the real-time data streaming environment. Existing mining classifiers therefore need to be updated frequently to adopt the changes in data streams. In this ...
Enhanced Classification Accuracy on Naive Bayes Data Mining Models
(International Journal of Computer Applications, 2011-08-01)
A classification paradigm is a data mining framework containing all the concepts extracted from the training dataset to differentiate one class from other classes existed in data. The primary goal of the classification ...
Classification by Clustering (CbC): An Approach of Classifying Big Data based on Similarities
Data classification in supervised learning is the process of classifying data for data mining task that helps to analyses data for decision making. The objective of a classification model is to correctly predict the ...
Active Learning with Clustering for Mining Big Data
Big data mining is become a key research issue nowadays. It's costly and also time-consuming to extract knowledge from big data. Big data is so big, it contains millions of data points that's why it's very difficult to ...
Solving Multi-Class Classification Tasks with Classifier Ensemble based on Clustering
Ensemble learning is very popular for few decades for solving classification problems, because it generates and combines a diversity of classifiers using the same learning algorithm for the base-classifiers. In this paper ...