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Machine learning (ML) algorithms have been shown to be effective in classifying a broad range of applications in the Internet traffic. In this paper, we propose algorithms and architectures to realize online traffic classification using flow level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL (Minimum Description Length) discretization algorithm...
Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to...
In order to survive the fierce competition in today's telecommunication industry, it is mandatory to understand the need of customers who might think to move toward another competitor. Thus, assessing the churn prediction, which becomes a real concern in the telecom industry, is critical in predicting future trends of the industry. In this work, we wanted to determine the best and reliable prediction...
In recent years, the use of machine learning methods to deal with the problem of user interest prediction has become a hot research direction in the field of electronic commerce. In the present stage, a naive Bayesian algorithm has the advantages of simple implementation and high classification efficiency. However, this method is too dependent on the distribution of samples in the sample space, and...
Diabetic Retinopathy is human eye disease which causes damage to retina of eye and it may eventually lead to complete blindness. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. Many physical tests like visual acuity test, pupil dilation, optical coherence tomography can be used to detect diabetic retinopathy but are time consuming and affects patients as...
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
Machine learning algorithms are computer programs that try to predict cancer type based on the past data. The eventual goal of Machine learning algorithms in cancer diagnosis is to have a trained machine learning algorithm that gives the gene expression levels from cancer patient, can accurately predict what type and severity of cancer they have, aiding the doctor in treating it. The existing technology...
A diamond adsorption detecting system based on machine learning is presented in this paper. The paper describes the system from the perspective of hardware and software design, and presents the image processing and machine learning algorithms applied in the system. The hardware includes three major parts — the camera, light source and support platform. The software includes modules of image acquisition,...
Gesture identification plays a vital role in today's human-computer interaction. In this paper, we proposed a sensor based gesture recognition system which makes the teacher to write in Telugu language on digital board from anywhere within the class room. Various classification algorithms k-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Decision tree are individually used for hand gesture...
Nowadays, the classification problems have become more challenging due to the various types of data set. Some data are appropriated for machine learning techniques and some data are appropriated for statistical leaning techniques. This work proposes a new hybrid ensemble of machine and statistical learning models using confidence-based boosting. The proposed method which uses variants of based classifiers...
In the paper, we extend the well-known golden-section search (GSS) method to make an unprecedented attempt to do discrete sequence searches. The GSS method is originally used to find the extremum of a strictly unimodal continuous function. We apply it on searching the best threshold for discretizing continuous attribute data in decision tree problems. Compared to typical methods, the shortcomings...
Changes in the network topology such as large-scale power outages or Internet worm attacks are events that may induce routing information updates. Border Gateway Protocol (BGP) is by Autonomous Systems (ASes) to address these changes. Network reachability information, contained in BGP update messages, is stored in the Routing Information Base (RIB). Recent BGP anomaly detection systems employ machine...
In order to compare the classification accuracies and performance differences between traditional and probability-based decision tree classifiers, and come to understand those algorithms, which aim to improve construction efficiency of probability-based decision trees, mentioned in "Decisions Trees for Uncertain Data", this paper tested several algorithms, named AVG, UDT, UDT-BP, UDT-LP,...
This paper presents a novel and efficient decision tree construction approach based on C4.5. C4.S constructs decision tree with information gain ratio and deals with missing values or noise. ID3 and its improvement, C4.5, both select one attribute as the splitting criterion each time during constructing decision tree, adopting one step forward. Comparing with one step forward, the proposed algorithm,...
This investigation reports the improved method for the text based emotion classification and prediction using a customized decision tree algorithm. Machine learning techniques such as Decision tree algorithm are widely used in research fields of bioinformatics, data mining, capturing knowledge in expert systems and so on. The emotions can be deducted from the online chat conversation and tagged. In...
In this research, Bagging algorithm that incorporates different classifier into classifier ensembles models for pixel classification is suggested. We chose classifier ensembles with decision trees, as the base classifiers. In the problem of pixel classification, experimental results demonstrate the effectiveness of the Bagging with forest of random trees (RandomForest) as base classifier compared...
Kernel learning is an important learning framework in machine learning, whose main idea is a mapping from input space to feature space induced by kernel function which yields a linear separation problem in the feature space. However, the generalization ability of kernel learning, which may lead to over-fitting of training data, has not been formally taken into consideration in previous literatures...
Datasets used in financial distress forecast are unbalanced. The traditional method gets lower predict accuracy especially in small samples of unbalanced datasets. The datasets are balanced with SMOTE method and then classified with the classical decision tree algorithm C4.5. The results show that the prediction model based on C4.5 algorithm gets the better performance.
A decision tree is a tree whose internal nodes can be taken as tests (on input data patterns) and whose leaf nodes can be taken as categories (of these patterns). These tests are filtered down through the tree to get the right output to the input pattern. Decision Tree algorithms can be applied and used in various different fields. It can be used as a replacement for statistical procedures to find...
Discrimination of benign and malignant mammographic masses based on supervised and unsupervised learning methods help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram. For predicting the outcomes of breast biopsies, we propose Rotation Forest with twelve decision trees algorithms as base classifiers and Principal Component Analysis (PCA) as filter...
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