Sampling is a broadly used method for handling the class imbalance problem. The concept of sampling is to change the distribution of examples so that the rare class is well defined in the training set. There are various techniques for sampling such as undersampling, oversampling, and a hybrid of both ... Read More

Random forest is a class of ensemble approaches particularly designed for decision tree classifiers. It integrates the predictions made by several decision trees, where each tree is created based on the values of a separate set of random vectors.The random vectors are produced from a constant probability distribution, unlike the ... Read More

The concept is to build multiple classifiers from the initial data and then aggregate their predictions when describing unknown examples. The ensemble of classifiers can be constructed in several methods which are as follows −By manipulating the training set − In this method, multiple training sets are generated by resampling ... Read More

A classification approach that has received considerable scrutiny is the support vector machine (SVM). This approach has its roots in statistical learning theory and has displayed promising empirical outcomes in several practical applications, from handwritten digit identification to text classification.SVM also operates with high-dimensional data and prevents the curse of ... Read More

An artificial neural network is a system placed on the functions of biological neural networks. It is a simulation of a biological neural system. The feature of artificial neural networks is that there are several structures, which required several approaches of algorithms, but regardless of being a complex system, a ... Read More

An artificial neural network is a system based on the functions of biological neural networks. It is a simulation of a biological neural system. The feature of artificial neural networks is that there are several structures, which required several methods of algorithms, but regardless of being a complex system, a ... Read More

An artificial neural network has a more complicated mechanism than that of a perceptron model. There are several methods in multilayer artificial neural networks which are as follows −The network can include multiple intermediary layers between its input and output layers. Such intermediary layers are known as hidden layers and ... Read More

An artificial neural network is a system placed on the functions of biological neural networks. It is a simulation of a biological neural system. The feature of artificial neural networks is that there are several structures, which required several approaches of algorithms, but regardless of being a complex system, a ... Read More

The naıve Bayesian classifier creates the assumption of class conditional independence, i.e., given the class label of a tuple, the values of the attributes are considered to be conditionally separate from one another. This defines evaluations.When the assumption affects true, hence the naïve Bayesian classifier is effective in contrast with ... Read More

Bayesian classifiers are statistical classifiers. It can predict class membership probabilities, such as the probability that a given sample applied to a definite class. Bayesian classifiers have also displayed large efficiency and speed when they can have high databases.Because classes are defined, the system must infer rules that supervise the ... Read More