In recent years, many new techniques have been proposed to overcome the challenges of data stream mining. In this paper, we review the operation of popular streaming algorithms highlighting their strength and w
Selecting attributes is prepared by the combination of attribute evaluation and search method using the WEKA Machine Learning Tool. The proposed method is performed in three phases. In the first step, support vector classifiers are implemented with four different kernel methods such as linear function...
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...
In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31(3):606-660. 2017) compared 18 Time Series Classification (TSC) al
Hemalatha," Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set", Bonfring International Journal of Man Machine Interface, Vol. 1, Special Issue, December 2011C. Lakshmi Devasena, Sumathi.T, Gomathi.V.V and Hemalatha.M,"Effectiveness Evaluation of Rule ...
Establishing an integrated classifier through multiple base classifiers, and the data in the training set is imported into the integrated classifier to realize the evaluation of the teaching mode 3.2. Design of the Evaluation System Using Decision Tree Generation Algorithm The decision tree generation al...
We compared the performance of classification in three factors: 1) taxonomy levels of features, 2) four classifiers and 3) feature selection methods. Results Preprocessing of data to reduce biases from meta-analysis Metagenome data from 1,079 individuals were collected for the healthy (control ...
The last section explained how scores are derived from classifiers. In most code I’ve seen, when you ask for a probability estimate you get one of these scores. They’re even called probability estimates in the documentation. But are they? The answer is: “Well, they’re estimates, but ...
For unbalanced credit data, class imbalance refers to the fact that the number of good credit borrowers is much larger than the number of bad credit borrowers. Most standard classifiers are dominated by majority class and ignore minority class in unbalanced data [16]. A large number of minority...
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30 MathSciNet MATH Google Scholar Djenouri Y, Zimek A (2018) Outlier detection in urban traffic data. In Proceedings of the 8th International Conference on Web Intelligence, Mining and ...