Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec

Ensemble classification is an information mining approach which utilizes various classifiers that cooperate for distinguishing the class label for new unlabeled thing from accumulation. Arbitrary Forest approach joins a few randomized choice trees and totals their forecasts by averaging. It has grabbed well-known attention from the community of research because of its high accuracy and superiority which additionally increase the performance. Now in this paper, we take a gander at improvements of Random Forest from history to till date. Our approach is to take a recorded view on the improvement of this prominently effective classification procedure. To begin with history of Random Forest to main technique proposed by Breiman then successful applications that utilized Random Forest and finally some comparison with other classifiers. This paper is proposed to give non specialists simple access to the principle thoughts of random forest.

A Review on Random Forest: An Ensemble Classifier

The appearance of new emerging multimedia services have created new challenges for cloud service providers, which have to react quickly to end-users experience and offer a better Quality of Service (QoS). Cloud service providers should use such an intelligent system that can classify, analyze, and adapt to the collected information in an efficient way to satisfy end-users' experience. This paper investigates how different factors contributing the Quality of Experience (QoE), in the context of video streaming delivery over cloud networks. Important parameters which influence the QoE are: network parameters, characteristics of videos, terminal characteristics and types of users' profiles. We describe different methods that are often used to collect QoE datasets in the form of a Mean Opinion Score (MOS). Machine Learning (ML) methods are then used to classify a preliminary QoE dataset collected using these methods. We evaluate six classifiers and determine the most suitable one for the task of QoS/QoE correlation.

Empirical study based on machine learning approach to assess the QoS/QoE correlation

This research investigates the problem of news articles classification. The classification is performed using N-gram textual features extracted from text and visual features generated from one representative image. The application domain is news articles written in English that belong to four categories: Business-Finance, Lifestyle-Leisure, Science-Technology and Sports downloaded from three well-known news web-sites (BBC, Reuters, and TheGuardian). Various classification experiments have been performed with the Random Forests machine learning method using N-gram textual features and visual features from a representative image. Using the N-gram textual features alone led to much better accuracy results (84.4%) than using the visual features alone (53%). However, the use of both N-gram textual features and visual features led to slightly better accuracy results (86.2%). The main contribution of this work is the introduction of a news article classification framework based on Random Forests and multimodal features (textual and visual), as well as the late fusion strategy that makes use of Random Forests operational capabilities.

https://mklab.iti.gr/files/IRFC%202014-Draft.pdf

News Articles Classification Using Random Forests and Weighted Multimodal Features

To defend and protect web server from the attack, it is important to know the nature and the behaviour of legitimate and illegitimate clients It is also important to provide access to the legitimate clients and provide a defence system against illegitimate clients The Distributed Denial of Service (DDoS) attack is a critical threat to the Internet By using its application layer protocol DDoS can cause a massive destruction by silently making an entrance to the web server as it act as one of the legitimate clients The paper uses parameter of the network packet like http GET, POST request and delta time to compute the accuracy in finding out the possible attack We use different classifiers like Naive Bayes, Naive Bayes Multinomial, Multilayer Perception, RBF network, Random Forest etc to classify the attack generated dataset We compare the accuracy, true positive rate, false positive rate of each algorithm by finding the confusion matrix

An Approach of DDOS Attack Detection Using Classifiers

Web page classification is the automated assigning of predefined subject category to the document. Automatic web page classification is one of the most essential techniques for Web Mining given that the web is a huge repository of various information including images, videos etc. And there is a need for categorization web pages to satisfy user needs. The classification of web pages into each category exclusively relies on man power which cost much time and effort. To alleviate this manually classification problem, more researchers focus on the issue of web pages classification technology. In this paper, we proposed Random Forest Classifier (RF) based on random forest method for multi-category web page classification. The proposed RF classifier can classify web pages efficiently according to their corresponding class without using other feature selection methods. We compared the accuracy of the proposed approach to decision tree classifier using in the same Yahoo web pages. The experiments have shown that the proposed approach is suitable for the multi-category web page classification.

Random forest classifier for multi-category classification of web pages

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Random forest classifier for multi-category classification of web pages