Bio: Reza Monsefi is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topic(s): Support vector machine & Artificial neural network. The author has an hindex of 16, co-authored 79 publication(s) receiving 755 citation(s).
Papers published on a yearly basis
TL;DR: An online Neural Network (NN) model, is composed of two different parts for handling concept drift and class imbalance, which is handled with a forgetting function and a specific error function which assigns different importance to error in separate classes.
Abstract: “Concept drift” and class imbalance are two challenges for supervised classifiers. “Concept drift” (or non-stationarity) is changes in the underlying function being learnt, and class imbalance is a vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Previous methods for classifying non-stationary and imbalanced data streams mainly focus on batch solutions, in which the classification model is trained using a chunk of data. Here, we propose an online Neural Network (NN) model. The NN model, is composed of two different parts for handling concept drift and class imbalance. Concept drift is handled with a forgetting function and class imbalance is handled with a specific error function which assigns different importance to error in separate classes. The proposed method is evaluated on 3 synthetic and 8 real world datasets. The results show statistically significant improvement to previous online NN methods.
01 May 2015-Pattern Recognition
TL;DR: This work proposes an instance reduction method based on hyperrectangle clustering, called Instance Reduction Algorithm using Hyperrectangle Clustering (IRAHC), which removes non-border (interior) instances and keeps border and near border ones.
Abstract: In instance-based classifiers, there is a need for storing a large number of samples as training set. In this work, we propose an instance reduction method based on hyperrectangle clustering, called Instance Reduction Algorithm using Hyperrectangle Clustering (IRAHC). IRAHC removes non-border (interior) instances and keeps border and near border ones. This paper presents an instance reduction process based on hyperrectangle clustering. A hyperrectangle is an n-dimensional rectangle with axes aligned sides, which is defined by min and max points and a corresponding distance function. The min–max points are determined by using the hyperrectangle clustering algorithm. Instance-based learning algorithms are often confronted with the problem of deciding which instances must be stored to be used during an actual test. Storing too many instances can result in a large memory requirements and a slow execution speed. In IRAHC, core of instance reduction process is based on set of hyperrectangles. The performance has been evaluated on real world data sets from UCI repository by the 10-fold cross-validation method. The results of the experiments have been compared with state-of-the-art methods, which show superiority of the proposed method in terms of classification accuracy and reduction percentage.
01 Dec 2013-Neurocomputing
TL;DR: An online ensemble of neural network (NN) classifiers with main contribution is a two-layer approach for handling class imbalance and non-stationarity, and cost-sensitive learning is embedded into the training phase of the NNs.
Abstract: Concept drift (non-stationarity) and class imbalance are two important challenges for supervised classifiers. ''Concept drift'' (or non-stationarity) refers to changes in the underlying function being learnt, and class imbalance is a vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Research on classification of non-stationary and imbalanced data streams, mainly focuses on batch solutions, whereas online methods are more appropriate. Here, we propose an online ensemble of neural network (NN) classifiers. Ensemble models are the most frequent methods used for classifying non-stationary and imbalanced data streams. The main contribution is a two-layer approach for handling class imbalance and non-stationarity. In the first layer, cost-sensitive learning is embedded into the training phase of the NNs, and in the second layer a new method for weighting classifiers of the ensemble is proposed. The proposed method is evaluated on 3 synthetic and 8 real-world datasets. The results show statistically significant improvement compared to online ensemble methods with similar features.
05 May 2010
TL;DR: In this paper, a solution based on NSGA-II is proposed for energy efficient QoS routing in cluster based WSNs, which outperforms network performance by optimizing multiple QoS parameters and energy consumption.
Abstract: With the growing demand for real time services in Wireless Sensor Networks (WSNs), quality of service (QoS) based routing has emerged as an interesting research topic. But offering some QoS guarantee in sensor networks raises significant challenges. The network needs to cope with battery constraints, while providing QoS (end-to-end delay and reliability) guarantees. Designing such QoS routing protocols that optimize multiple objectives is computationally intractable. Higher power relay nodes can be used as cluster heads in a two-tiered WSN and these relay nodes may form a network among themselves to route data towards the sink. In this model, the QoS guarantee is determined mainly by these relay nodes. In this paper a solution based on NSGA-II is proposed for energy efficient QoS routing in cluster based WSNs. Simulation results demonstrate that the proposed protocol outperforms network performance by optimizing multiple QoS parameters and energy consumption.
01 May 2012-Computer Networks
TL;DR: Simulation results indicate that Queen-MAC prolongs the network lifetime while increasing the average delivery ratio and keeping the transmission latency low.
Abstract: Major problems in the Medium Access Control (MAC) of Wireless Sensor Networks (WSNs) are: sleep/wake-up scheduling and its overhead, idle listening, collision, and the energy used for retransmission of collided packets. This paper focuses on these problems and proposes an adaptive quorum-based MAC protocol, Queen-MAC. This protocol independently and adaptively schedules nodes wake-up times, decreases idle listening and collisions, increases network throughput, and extends network lifetime. Queen-MAC is highly suitable for data collection applications. A new quorum system, dygrid is proposed that can provide a low duty cycle, O(1/n), for adjusting wake-up times of sensor nodes. Theoretical analysis demonstrates the feasibility of dygrid and its superiority over two commonly used quorum systems (i.e., grid and e-torus). A lightweight channel assignment method is also proposed to reduce collision and make concurrent transmissions possible. Simulation results indicate that Queen-MAC prolongs the network lifetime while increasing the average delivery ratio and keeping the transmission latency low.
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
01 Jan 1980
••01 May 1975
TL;DR: The Fundamentals of Queueing Theory, Fourth Edition as discussed by the authors provides a comprehensive overview of simple and more advanced queuing models, with a self-contained presentation of key concepts and formulae.
Abstract: Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.
TL;DR: Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision.
Abstract: Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.
TL;DR: An in depth review of rare event detection from an imbalanced learning perspective and a comprehensive taxonomy of the existing application domains of im balanced learning are provided.
Abstract: 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.