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A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale...
A novel feature extraction method for hand gesture recognition from sequences of image frames is described and tested. The proposed method employs higher order local autocorrelation (HLAC) features for feature extraction. The features are extracted using different masks from Grey-scale images for characterising hands image texture with respect to the possible position, and the product of the pixels...
In multi-label data, each instance belongs to a set of labels, instead of one label. Due to the increasing number of modern applications for multi-label data, multi-label classification has attracted the attention of many researchers. Similar to single label data, eliminating irrelevant and/or redundant features plays an important role in improving the classifier performance. In this paper, meta-heuristic...
The need of machine learning in the defence planning and strategies is increasing day by day due to the increasing amount of breaches and decimations caused by terrorist forces. A myriad of military bases, temporary campaigns, base camps etc. are being targeted and attacked by several terrorist forces. The common problem in the warfare and tumultuous international borders is the frequent and violent...
Feature selection process involves identifying a subset of features that provides same results as the original entire set of features. Feature subset selection removes irrelevant and redundant features for reducing data dimensionality. Feature selection, also known as attribute subset selection. A feature selection algorithm can be measured from both the efficiency and effectiveness points. The efficiency...
Cardiac ailments like Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are life threatening conditions which require prompt medical treatment. Automatic External Defibrillator (AED) is a device that delivers shock when it encounters VF and rapid VT signals. In this paper we have attempted to develop an algorithm for AED that detects normal sinus rhythm, Ventricular fibrillation and ventricular...
In the current engross world, traffic overflow is a common problem for the metropolises. In spite of increasing the size of transportation systems and prompting the public transportation may increase the traffic overflow. This kind of traffic overflow problem cannot be solved manually. Today the traffic data has been entered and erupted the time of huge transportation of the data. Hence it is important...
The random forest algorithm is a new classification and prediction model algorithm. So far, there is not much research on the problem of unbalanced data for random forest classification, ditto, no direct and effective method. On the basis of feature selection algorithm based on correlation measure, the integration feature selection method was helpful to increase the selection probability of classification...
The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initial terrain classification. Then an improved fuzzy c-means algorithm was applied on classification, and it included optimization of determine clustering center, got the number of clustering...
In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and combines their outcomes to make an estimation. The simplest form of ensemble learning...
Document layout segmentation and recognition is an important task in the creation of digitized documents collections, especially when dealing with historical documents. This paper presents an hybrid approach to layout segmentation as well as a strategy to classify document regions, which is applied to the process of digitization of an historical encyclopedia. Our layout analysis method merges a classic...
Solar power penetration has made the site-specific energy ratings an essential necessity for utilities, independent systems operators and regional transmission organizations. Since, it leads to the reliable and efficient energy production with the increased levels of solar power integration. This study concentrates on the partitional clustering analysis of monthly average insolation period data for...
Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for...
Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built...
In this study, a modified ant colony optimization algorithm has been proposed to find a feature subset most relevant to the classification task. The algorithm incorporates a new heuristic information component based on classification accuracy. The proposed methodology has been applied in a multiclass classification problem of capsule endoscopic images, where image regions will be classified as bleeding,...
IPTV is an increasingly important service for telecom operators. Service providers have to repair IPTV faults quickly in order to provide high quality service. It will be very helpful if faults can be predicted. Considering this situation, we combine status data from the set top box with the data of customer trouble tickets and then build a prediction system with improved AdaBoost to predict faults...
The dramatic increase in the network traffic data has become a major concern in security systems. Intrusion detection systems (TDSs), as common widely used security systems for communication networks, are not an exception. An IDS monitors the network traffic to detect attacks through classifying the network traffic data into normal and abnormal classes. Due to the high dimensionality of the network...
The exponential growth of unstructured messages generated by the computer systems and applications in modern computing environment poses a significant challenge in managing and using the information contained in the messages. Although these data contain a wealth of information that is useful for advanced threat detection, the sheer volume, variety, and complexity of data make it difficult to analyze...
A new method for feature selection based on improved maximal relevance and minimal redundancy (mRMR) is proposed in this paper. In order to describe the influence of the added features on correlation between candidate features subset and decision, the standard mRMR was improved by introducing the calculation of parameter Sig ≥ (a, B, D). The value of Sig ≥ (a, B, D) is used to determine whether a...
In this study we investigated the effect of mastery confidence manipulation on BCI performance and P300 amplitude. We used a 6×6 P300 speller matrix and participants spelled words containing five letters each. Using a cover story, thirty-six participants were misinformed that they would use classification algorithms of diverse difficulty in three experimental blocks (easy, medium, hard condition)...
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