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In the training of the radial basis function network (RBFN), feature selection and classifier design are two tasks commonly addressed in separated processes. The former is related to the number of input nodes, whereas the latter is associated with the design of the hidden layer. Hence, this paper presents an algorithm to train a RBFN based on differential evolution (DE), which simultaneously adjusts...
In this work we derive a novel clustering scheme for hyperspectral pixels according to the material they sense. We utilize statistical correlations that pixels sensing the same material exhibit. Specifically, kernel learning is combined with a norm-one regularized canonical correlations framework that can perform data clustering on nonlinearly dependent data. To tackle the derived minimization formulation...
Tag recommendation has gained significant popularity for annotating various web-based resources including web services. Compared with other approaches, tag recommendation based on supervised learning models usually lead to good accuracy. However, a high-quality training data set is needed, which demands manual tagging efforts from domain experts. While we could leverage the tags of existing web services...
Recently, the multi-label learning has drawn considerable attention as it has many applications in text classification, image annotation and query/keyword suggestions etc. In recent years, a number of remedies have been proposed to address this challenging task. However, they are either tree based methods which has the expensive train costs or embedding based methods which has relatively lower accuracy...
Self-Organizing Maps (SOMs) are unsupervised neural networks that build data models. Neuron labeling attaches descriptive textual labels to the neurons making up a SOM, and is an important component of SOM-based exploratory data analysis (EDA) and data mining (DM). Several neuron labeling approaches tend to leave some neurons unlabeled. The interaction between unlabeled neurons and SOM model accuracy...
Millions of computers are infected with bot malware, form botnets and enable botmaster to perform malicious and criminal activities. Intrusion Detection Systems are deployed to detect infections, but they raise many correlated alerts for each infection, requiring a large manual investigation effort. This paper presents a novel method with a goal of determining which alerts are correlated, by applying...
To protect computer networks from attacks and hackers, an intrusion detection system (IDS) should be integrated in the security architecture. Although the detection of intrusions and attacks is the ultimate goal, IDSs generate a huge amount of false alerts which cannot be properly managed by the administrator, along with many noisy alerts or outliers. Many research works were conducted to improve...
Network protocol classification plays an important role in modern network security and fine-grained management architectures. The state-of-the-art network protocol classification methods aim to take the advantages of flow statistical features and machine learning techniques. However the classification performance is severely affected by limited supervised information and unknown network protocols...
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
Network traffic anomaly detection can help to early detect network attacks because hacker's activities may result in unusual changes of network traffic, that are significant fluctuations compared to normal traffic of the network Among various anomaly detection approaches, principal component analysis (PCA) has been seen as an effective solution. Until now, PCA is basically applied to dimension reduction...
Recently there has been great interest in the application of word representation techniques to various natural language processing (NLP) scenarios. Word representation features from techniques such as Brown clustering or spectral clustering are generally computed from large corpora of unlabeled data in a completely unsupervised manner. These features can then be directly included as supplementary...
Social networks, and the behaviour of groups of online users, are popular topics in modeling and classifying Internet traffic data. There is a need to analyze online network performance metrics through suitable workload benchmarks. We address this issue with a Multi-dimensional Hidden Markov Model (MultiHMM) to act as a Multi-User workload classifier. The MultiHMM is an adaptation of the original...
Canonical correlation analysis (CCA) has been widely applied to information fusion. It only considers the correlated information of the paired data, but ignores the correlated information between the samples in the same class. Furthermore, class information is useful for CCA, but there is little class information in the scenarios of real applications. Thus, it is difficult to utilize the correlated...
Approaches to imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of hidden structure within the majority class. The aim of this paper is to highlight the effect of sub-clusters within the majority class on detecting minority class instances, and handle imbalanced classification by learning the structure in the data. We propose a decomposition based...
Multi-label classification has attracted much attention in recent years due to various applications in real world. There have been many algorithms to deal with this problem. However, there is no algorithm that simultaneously exploits the locality in the instance space and label space which plays an important role in generating a satisfactory model. In this paper we present such an algorithm. It utilizes...
This paper presents a new semi-supervised method to effectively improve traffic classification performance when few supervised training data are available. Existing semi supervised methods label a large proportion of testing flows as unknown flows due to limited supervised information, which severely affects the classification performance. To address this problem, we propose to incorporate flow correlation...
This paper presents an automatic image annotation approach that integrates the random forest classifier with particle swarm optimization algorithm for classes' scores weighting. The proposed hybrid approach refines the output of multi-class classification that is based on the usage of random forest classifier for automatically labeling images with a number of words. Each input image is segmented using...
The data mining technology is more and more widely used in the telecom industry. But telecom data set always includes instances with missing values. Besides, many data mining models are sensitive for the missing value and distortion. Estimating missing values becomes an inherent problem. To address the problem, A prediction method is proposed for the missing value based on the BP neural network and...
In this paper, a new abnormal activity detection algorithm is proposed for multi-camera surveillance applications. The proposed algorithm models the entire scene covered by the multi-camera system as a network. In this network, each node corresponds to a segmentation of the entire scene and each edge represents the activity correlation between the corresponding segmentations. Based on this network,...
Succeeding in determining information about the origin of a digital image is a basic issue of multimedia forensics. In particular it could be interesting to individuate which is the specific camera (brand and/or model) that has taken that photo; to do that, additional knowledge are needed about the camera such as its fingerprint, usually computed by resorting at the extraction of the PRNU (Photo-Response-Uniformity-Noise)...
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