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This paper addresses energy-efficient data gathering issues in wireless sensor networks (WSNs). Leveraging data correlation in densely-deployed sensor networks, we propose an Energy-aware Probability-based Clustering algorithm (EPC), featuring high scalability and flexibility particularly suitable for large-scale WSNs. Unlike most existing data gathering schemes that construct static routing structures...
Recently traffic identification based on Machine Learning (ML) techniques has attracted a great deal of interest. Two challenging issues for these methods are how to deal with encrypted flows and cope with the rapid growing number of new application types correctly and early. We propose a hybrid traffic identification method and a novel unsupervised clustering algorithm, On-Line Density Based Spatial...
The main goal in the credit scoring process is forecasting every customer's adequacy in accomplishment of their obligations precisely as much as possible. Although this technique is identical with regular binary classification tasks but it has a few crucial differences. Whereas, based on financial credit rules, a customer is considered based on a degree of goodness or badness, one cannot allocate...
In this paper, a novel approach for classifying algal images was presented, which is used in flow-cytometry-based real-time red tide monitoring system. Firstly, an ensemble of support vector machines (SVMs) was trained and the test samples were labeled by them based on the summation of negative probability (SNP). Secondly, those samples most likely mistakenly labeled were picked out and re-labeled...
Probabilistic Neural Networks (PNN) learn quickly from examples in one pass and asymptotically achieve the Bayes-optimal decision boundaries. The major disadvantage of PNN is that it requires one node or neuron for each training sample. Various clustering techniques have been proposed to reduce this requirement to one node per cluster center. Decision boundaries of clustering centers are approximation...
We address the problem of Kannada character recognition, and propose a recognition mechanism based on k-means clustering. The large dataset of Kannada characters and their similarity makes the problem one order of magnitude more difficult than for a standard language like English. We propose a segmentation technique to decompose each character into components from 3 base classes, thus reducing the...
This paper proposes an improved genetic algorithm, it keeps the population diversity by similarity checks on the population before selection, and the algorithm solves the early-maturing problem of the population evolution, and proposes a formula for mutation probability related with similarity rate and iteration times. The algorithm not only maintains a good diversity of population, but also guarantees...
There is a growing concern about the increasing vulnerability of future computing systems to errors in the underlying hardware. Traditional redundancy techniques are expensive for designing energy-efficient systems that are resilient to high error rates. We present Error Resilient System Architecture (ERSA), a low-cost robust system architecture for emerging killer probabilistic applications such...
In this paper, we describe a two-stage hybrid approach to select gene features and produce dominant patterns for evaluating the pathological probability. To discover suitable genes as experiment samples for distinguishing the status of gene regulation, we utilized receiver operating characteristic (ROC) method to eliminate non-significant genes of unapparent variation between normal tissues and tumors...
In iterative refinement clustering algorithms, such as the various types of K-Means algorithms, the clustering results are very sensitive to the initial cluster centers. Conventional initialization methods tend to loss effectiveness due to the so-called "curse of dimensionality" when clustering high-dimensional data. In this paper, a local density based method is proposed to search for initial...
This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion...
RF-based transceiver-free object tracking, originally proposed by the authors, allows real-time tracking of a moving object, where the object does not have to be equipped with an RF transceiver. Our previous algorithm, the best cover algorithm, suffers from a drawback, i.e., it does not work well when there are multiple objects in the tracking area. In this paper, we propose a localization model of...
This paper presents PWEM, a technique for detecting class label noise in training data. PWEM detects mislabeled examples by assigning to each training example a probability that its label is correct. PWEM calculates this probability by clustering examples from pairs of classes together and analyzing the distribution of labels within each cluster to derive the probability of each label's correctness...
In recent years there has been a growing interest in clustering uncertain data. In contrast to traditional, "sharp" data representation models, uncertain data objects can be represented in terms of an uncertainty region over which a probability density function (pdf) is defined. In this context, the focus has been mainly on partitional and density-based approaches, whereas hierarchical clustering...
Minimax Probability Machine (MPM), learning a decision function by minimizing the maximum probability of misclassification, has demonstrated very promising performance in classification and regression. However, MPM is often challenged for its slow training and test procedures. Aiming to solve this problem, we propose an efficient model named Minimax Clustering Probability Machine (MCPM). Following...
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