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Class imbalance presents a problem when traditional Classification algorithms are applied .In the previous years there are most important substitution and change has been carried out on data classification. Classification of data becomes difficult because of its unbalanced nature. The problem of imbalance class has developed into significant data mining issue. The class imbalance situation arises...
In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more than one class labels. The traditional binary and multi-class classification problems are the subset of the multi-label problem with the number of labels corresponding to each sample...
Affective interaction is a new emerging area of interest for interaction designers. This research explores the potential of our hybrid approach that relies on both, lexical and machine learning techniques for detection of Ekman's six emotional categories in user's text. The initial results of the performance evaluation of the proposed hybrid approach are encouraging and comparable to related research...
This paper concerns dynamic provisioning of cloud resources performed by an intermediary enterprise that provides a private cloud (also referred to as a virtual private cloud) for a single client enterprise using resources acquired on demand from a public cloud. A new proactive technique for auto-scaling of resources that changes the number of resources for the private cloud dynamically based on system...
The analog/RF functional test which is based on specification circuit testing is very costly due to lengthy test times and highly sophisticated test equipment. Alternative test measures, extracted by means of Built-in Self Test (BIST) techniques, are a promising approach to replace standard specification-based tests. However, these test measures must be evaluated at the design stage by estimating...
Learning to rank is an important task for many data mining applications. Essentially, the goal of learning to rank is to learn an appropriate similarity or distance metric to determine the relevance relationships among data points. However, most of the existing approaches for distance metric learning are limited in three aspects. First, they often assume a fixed form of distance metric for the entire...
The large number of genes found in most gene micro array datasets demands the use of feature selection techniques to alleviate this problem of high-dimensionality. However, the computational cost of filter-based subset evaluation techniques such as Correlation-Based Feature Selection (CFS) has generally limited the use of these techniques to smaller datasets, or at least smaller collections of gene...
One of the most challenging problems encountered when analyzing real-world gene expression datasets is high dimensionality (overabundance of features/attributes). This large number of features can lead to suboptimal classification performance and increased computation time. Feature selection, whereby only a subset of the original features are used for building a classification model, is the most commonly...
One of the more prevalent problems when working with bioinformatics datasets is class imbalance, when there are more instances in one class compared to the other class (es). This problem is made worse because frequently, the class of interest is also the minority class. A possible solution is data sampling, a powerful tool for combating class imbalance by adding or removing instances to make the dataset...
Multiple Instance Learning (MIL) has been an interesting topic in the machine learning community. Since proposed, it has been widely used in content-based image retrieval and classification. In the MIL setting, the samples are bags, which are made of instances. In positive bags, at least one instance is positive. Whereas negative bags have all negative instances. This makes it different from the supervised...
Two methodologies for neurally mediated syncope (NMS) prediction, based on the joint analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG), are compared. Several features that characterize the variations in the inotropic, chronotropic, vascular tone and blood pressure surrogates were extracted and fed into two prediction models. The first method is based on the combination of the Minkowski...
Since the early stages of the introduction of DNA microarray technology, there has been an enormous interest on clinical application for various diseases diagnosis. Microarray data classification is a difficult task for biologists due to its small sample sizes combined to its high number of features increasing the risk of overfitting. In the past years tools have been developed to extract biological...
Due to its simplicity and intuitivity, the k-nearest neighbor method is one of the most commonly used technique to address different classification problems. However, to apply such a classification technique, a distance metric is to be considered to define a certain distance in the feature space. Usually classic norms such as Minkowski, Mahalanobis, etc. Are used, but even though they can be applied...
Kinship verification from facial images in wild conditions is a relatively new and challenging problem in face analysis. Several datasets and algorithms have been proposed in recent years. However, most existing datasets are of small sizes and one standard evaluation protocol is still lack so that it is difficult to compare the performance of different kinship verification methods. In this paper,...
Kernel-based methods have been widely used in various machine learning tasks. The performance of these methods strongly relies on the choice of the kernel which represents the similarity between each pair of data points. Therefore, choosing an appropriate kernel function or tuning its parameter(s) is an important issue in the kernel-based methods. Multiple Kernel Learning (MKL) methods have been developed...
Trust is one source of information that has been widely adopted to personalize online services for users, such as in product recommendations. However, trust information is usually very sparse or unavailable for most online systems. To narrow this gap, we propose a principled approach that predicts implicit trust from users' interactions, by extending a well-known trust antecedents framework. Specifically,...
People play different roles in various social networks. Even in a single network, people may interact with others based on different roles, and there are various relationships among them. However, current research usually treats all relationships homogeneously (i.e. friendship). In this paper, we try to identify different types of relationship (family, colleague, and social) within social networks...
In this paper we address the problem of predicting SPARQL query performance. We use machine learning techniques to learn SPARQL query performance from previously executed queries. Traditional approaches for estimating SPARQL query cost are based on statistics about the underlying data. However, in many use-cases involving querying Linked Data, statistics about the underlying data are often missing...
We present a novel framework for semisupervised labeling of regions in remote sensing image datasets. Our approach works by decomposing the image into irregular patches or superpixels and derives novel features based on intensity histograms, geometry, corner density, and scale of tessellation. Our classification pipeline uses either k-nearest neighbors or SVM to obtain a preliminary classification...
Automated and accurate biometrics identification using periocular imaging has wide range of applications from human surveillance to improving performance for iris recognition systems, especially under less-constrained imaging environment. Restricted Boltzmann Machine is a generative stochastic neural network that can learn the probability distribution over its set of inputs. As a convolutional version...
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