Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature methods. However, traditional such as Fisher Score, Laplacian relief use class labels inadequately. Previous based MIFS ignore the intra-class tight inter-class sparse property of samples. To address these p...

Feature Selection is important to improve learning performance, reduce computational complexity and decrease required storage. There are multiple methods for feature selection, with varying impact and computational cost. Therefore, choosing the right method for a given data set is important. In this paper, we analyze the advantages of metalearning for feature selection employment. This issue is...

The focus of this research is the application k-Nearest Neighbor algorithm in terms classifying botnet attacks IoT environment. kNN has several advantages classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well handling large datasets Bot-IoT dataset, which represents a huge amount data about on networks. Therefore, improving performance main c...

MapReduce is a software framework that allows certain kinds of parallelizable or distributable problems involving large data sets to be solved using computing clusters. This paper introduces our experience of grouping internet users by mining a huge volume of web access log of up to 500 gigabytes. The application is realized using hierarchical clustering algorithms with Map-Reduce, a parallel p...

Feature selection has been keen area of research in classification problem. Most of the researchers mainly concentrate on statistical measures to select the feature subset. These methods do not provide a suitable solution because the search space increases with the feature size. The FS is a very popular area for applications of populationbased random techniques. This paper suggests swarm optimi...

One of the most important issues in Text Categorization (TC) is Feature Selection (FS). Many FS methods have been put forward and widely used in TC field, such as Information Gain (IG), Document Frequency (DF) thresholding, Mutual Information (MI) and so on. Empirical studies show that IG is one of the most effective methods, DF performs similarly, in contrast, and MI had relatively poor perfor...

This paper puts forward a hierarchical approach for categorizing emails with the ME model based on its contents and properties. This approach categorizes emails in a two-phase way. First, it divides emails into two sets: legitimate set and Spam set; then it categorizes emails in two different sets with different feature selection methods. In addition, the pre-processing, the construction of fea...

Feature selection (FS) is a crucial procedure in machine learning pipelines for its significant benefits removing data redundancy and mitigating model overfitting. Since concept drift widespread phenomenon streaming could severely affect performance, effective FS on drifting streams imminent. However, existing state-of-the-art algorithms fail to adjust their strategy adaptively when the feature...

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...

Secrecy of decryption keys is an important pre-requisite for security of any encryption scheme and compromised private keys must be immediately replaced. Forward Security (FS), introduced to Public Key Encryption (PKE) by Canetti, Halevi, and Katz (Eurocrypt 2003), reduces damage from compromised keys by guaranteeing confidentiality of messages that were encrypted prior to the compromise event....