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To embed ensemble techniques into belief decision trees for performance improvement, the bagging algorithm is explored. Simple belief decision trees based on entropy intervals extracted from evidential likelihood are constructed as the base classifiers, and a combination of individual trees promises to lead to a better classification accuracy. Requiring no extra querying cost, bagging belief decision...
Example-based single image super-resolution (SISR) methods use external training datasets and have recently attracted a lot of interest. Self-example based SISR methods exploit redundant non-local self-similar patterns in natural images and because of that are more able to adapt to the image at hand to generate high quality super-resolved images. In this paper, we propose to combine the advantages...
We describe an end-to-end system for explainable automatic job candidate screening from video CVs. In this application, audio, face and scene features are first computed from an input video CV, using rich feature sets. These multiple modalities are fed into modality-specific regressors to predict apparent personality traits and a variable that predicts whether the subject will be invited to the interview...
This paper introduces a novel open access resource, the machine-readable phonetic dictionary for Romanian — MaRePhoR. It contains over 70,000 word entries, and their manually performed phonetic transcription. The paper describes the dictionary format and statistics, as well as an initial use of the phonetic transcription entries by building a grapheme to phoneme converter based on decision trees....
This paper introduces the application of attribute selection methods along with Bayes classifiers. The proposal has been evaluated in eleven binary and multi-class real data sets with a number of instances lower than a thousand and a number of attributes between eight and sixteen thousand. Among them, five data sets belong to the Bioinformatics area. Experiments show that, in general terms, the most...
A mixed pixel in remote sensed images is a major problem, and the super-resolution mapping is one of the approach to deal with this problem. In this paper, we address the problem of super-resolution mapping by combining a set of random forests with a Markov random field (MRF) model. Here, a random forest is trained to estimate a class proportion of only one land cover class. Thus, there are equal...
Nowadays, traffic surveillance systems are installed in major cities. They are usually used for two purposes, i.e. realtime traffic monitoring and archived events searching. For the latter purpose, the traffic surveillance systems can be used for police officers' benefits, such as vehicle identification in specific events including stolen vehicles or hit-and-run cases. In such circumstances, the officers...
Machine learning and data mining techniques have been widely used in order to improve network intrusion detection in recent years. These techniques make it possible to automate anomaly detection in network traffics. One of the major problems that researchers are facing is the lack of published data available for research purposes. The KDD'99 dataset was used by researchers for over a decade even though...
With the continued explosion of digitized data, data mining and data collection have become more prevalent. With this growth, we have also seen increased concern over data privacy and intellectual property. Within this environment, an important question has emerged: Can machine learning and data mining techniques be leveraged without compromising privacy? This paper revisits the concepts and techniques...
Categorical data exist in many domains, such as text data, gene sequences, or data from Census Bureau. While such data are easy for human interpretation, they cannot be directly used by many classification methods, such as support vector machines and others, which require underlying data to be represented in a numerical format. To date, most existing learning methods convert categorical data into...
Injection molded part quality can be improved by precise process adjustment, which could rely on in-situ measurements of part quality. Geometrical and appearance quality (visually and sensory) requirements are increasing. However, direct measurement is often not feasible industrially. Therefore, process control must rely on a prediction of parts quality attributes. This study compares prediction performances...
This paper proposes a novel learning-based image Super-Resolution via a Randomized Multi-split Forests model (SRRMF). The proposed method uses the LR-HR training patch pairs to model the nonlinear patch manifold into a pairs of linear subspaces. The key idea of this approach is to use several decision trees split randomly the training data into different classes. A linear regression model is learnt...
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...
Recently the traditional video surveillance systems of crowd scenes have been deployed in various areas of applications; health monitoring, security etc. Monitoring crowds and identifying their behaviors is one of the most interesting applications of visual surveillance as it is very difficult to assess crowds by human experts. In this paper, we present inter-group and intra-group properties of crowd...
Tor is an anonymous communication system that can protect our privacy, but it also provides a haven for criminals to avoid network tracing. Therefore, anonymous traffic analysis and classification is an important part of maintaining network security. Existing Tor traffic classification methods require a large number of labeled data, and the classification accuracy rate is not satisfied for practical...
Predicting the gap between taxi demand and supply in taxi booking apps is completely new and important but challenging. However, manually mining gap rule for different conditions may become impractical because of massive and sparse taxi data. Existing works unilaterally consider demand or supply, used only few simple features and verified by little data, but not predict the gap value. Meanwhile, none...
The incidence of hypertension associated with pregnancy contributes significantly to increase maternal and fetal deaths during pregnancy and childbirth. Due to its high incidence rate and several complications, the study of this disorder has been subject of numerous investigations in an attempt to determine its prevention and improve the treatment conduction. In this context, this paper uses a data...
We consider an attention-based model that recognizes objects via a sequence of glimpses, and analyze the variation in classification accuracy with the number of glimpses. The problem of object recognition is formulated as a partially observable Markov decision process where the environment is partially observable and glimpses are actions. We show that voting from random attentional policies provides...
Aero-engine fault diagnosis plays a crucial role in safe operation and cost-effective maintenance. Early detection and isolation of component faults prior to failure of aero-engines is of utmost importance. This paper applied various classification methods, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (K-NN) and Linear Discriminant Analysis (LDA), to aero-engine...
At present, several studies exist describing the relevance of human factor in air transport with main focus on pilots and flight safety. Within such studies, monitoring of physiological functions is used. There are lot of physiological parameters and methods of their assessment; however, they are mostly based on principles originating from clinical practice. Yet, sensitivity and specificity of these...
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