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In 2011, authors of the Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date". [2] It became quite popular after ranking #1 in the Top 10 Algorithms in Data Mining pre-eminent paper published by Springer LNCS in 2008. [3]
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License.It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".
Orange, a free data mining software suite, module Orange.ensemble Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost R package GBM (Generalized Boosted Regression Models) implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting ...
In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). For example, deciding on whether an image is showing a banana, an orange, or an ...
Frequent pattern mining. Itemsets [11] Graphs [12] Change detection algorithms [13] These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time. MOA supports bi-directional interaction with Weka. MOA is free software released under the GNU GPL.
Decision trees used in data mining are of two main types: Classification tree analysis is when the predicted outcome is the class (discrete) to which the data belongs. Regression tree analysis is when the predicted outcome can be considered a real number (e.g. the price of a house, or a patient's length of stay in a hospital).
Based on learning paradigms, the existing multi-label classification techniques can be classified into batch learning and online machine learning. Batch learning algorithms require all the data samples to be available beforehand. It trains the model using the entire training data and then predicts the test sample using the found relationship.
Data available in the project's website. Data is also available here. [367] Zampieri et al. Cyber reports from the National Cyber Security Centre This data is not pre-processed. Threat reports, reports and advisory, news, blog-posts, speeches. Alternate list of reports. [368] APT reports by Kaspersky This data is not pre-processed. [369] The ...