A new model for iris data set classification based on linear support vector machine parameter's optimization

Abstract

Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

“…It involves the use of FS algorithms to filter out irrelevant and redundant data features from the original dataset to prevent over-fitting [6,13] and improve the classification accuracy of the model. Feature selection also reduces the classification models' complexity in time and space domains [14][15][16][17][18]. The main idea of this paper is to employ the TLBO-based algorithm for features subset selection in BC diagnosis.…”

Abnormal activity detection in surveillance video scenes

“…The classification of (KNN), meant that the most anonymous pattern assigned the most prevalent classes among the nearest neighbors' classes. In [24,25] if there are two classes linked, the link of the lowest average distance assigned to the anonymous pattern [26,27].…”

Performance evaluation of two models in the reactive routing protocol in manets

“…Route Maintenance: is the correction process that is made through periodic signals, because a break in the link leads to a path failure, because the network topology changes frequently due to the mobility of the nodes [7]. The example of this protocol are the ad hoc on-demand distance vector routing (AODV), temporary ordered routing protocol (TORA), signal stability based adaptive routing protocol (SSA), dynamic source routing (DSR), associativity based routing (ABR) [17][18][19]. Proactive Routing Protocols Proactive routing protocols based on general structure information saved in the form of routing tables updated frequently and periodically with the change in network topology and stored in each node, and wired network routing protocols are an extension of them.…”