Anu G. Bourgeois

A genetic algorithm-based method for feature subset selection

As a commonly used technique in data preprocessing, feature selection selects a subset of informative attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the comprehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria has been introduced by researchers. However, it is discovered that no single criterion is best for all applications. In this paper, we propose a framework based on a genetic algorithm (GA) for feature subset selection that combines various existing feature selection methods. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. We conducted experiments using three data sets and three existing feature selection methods. The experimental results demonstrate that our approach is a robust and effective approach to find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm.

Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. First, we study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects data of a snapshot to the base station in a distributed manner without any time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Finally, extensive simulation results indicate that ADDC can effectively finish a data collection task and significantly reduce data collection delay.

A new GTS allocation scheme for IEEE 802.15.4 networks with improved bandwidth utilization

The IEEE 802.15.4 standard enables device level wireless connectivity in personal area networks. Its medium access control (MAC) protocol supports the exclusive use of a wireless channel through guaranteed time slot (GTS). However, the bandwidth underutilization problem occurs in GTSs when the used bandwidth is less than the available. In this paper, a new GTS scheme is presented to allow more devices to share the bandwidth within the same period. The evaluation and analysis reveals that the bandwidth utilization is improved.

Using a genetic algorithm approach to solve the dynamic channel-assignment problem

The Channel Assignment Problem is an NP-complete problem to assign a minimum number of channels under certain constraints to requested calls in a cellular radio system. Examples of the many approaches to solve this problem include using neural-networks, simulated annealing, graph colouring, genetic algorithms, and heuristic searches. We present a new heuristic algorithm that consists of three stages: 1) determine-lower-bound cell regular interval assignment; 2) greedy region assignment; and 3) genetic algorithm assignment. Through simulation, we show that our heuristic algorithm achieves lower bound solutions for 11 of the 13 instances of the well known Philadelphia benchmark problem. Our algorithm also has the advantage of being able to find optimum solutions faster than existing approaches that use neural networks.

Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay.

Improving Feature Subset Selection Using a Genetic Algorithm for Microarray Gene Expression Data

Microarray data usually contains a huge number of genes (features) and a comparatively small number of samples, which make accurate classification or prediction of diseases challenging. Feature selection techniques can help us identify important and irrelevant (unimportant) features by applying certain selection criteria. However, different feature selection algorithms based on various theoretical arguments often produce different results when applied to the same data set. This makes selecting an optimal or near optimal feature subset for a data set difficult. In this paper, we propose using a genetic algorithm to improve feature subset selection by combining valuable outcomes from multiple feature selection methods. The goal of our genetic algorithm is to achieve a balance between the classification accuracy and the size of the feature subsets selected. The advantages of this approach include the ability to accommodate different feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. The experimental results demonstrate that our approach can find subsets of features with higher classification accuracy and/or smaller size compared with each individual feature selection algorithm.

Delay efficient opportunistic routing in asynchronous multi-channel cognitive radio networks

In this paper, we are interested in designing efficient distributed opportunistic routing protocols for multi-hop multi-channel cognitive radio networks (CRNs). In CRNs, secondary users (SUs) access unused primary channels opportunistically, which induces considerable end-to-end delays for multi-hop routing. The primary cause of the delay overhead is that the set of available channels change dynamically over time due to the activities of primary users, making it challenging to effectively explore the spectrum diversity. Our approach towards working with such a dynamic network is to construct a cross-layer distributed opportunistic routing protocol. Our protocol jointly considers the channel sensing strategy, the forwarder selection for each SU, and the package division scheme on each link. We mathematically model the expected delay of each hop along the routing path. This delay model sheds lights on our expected end-to-end delay analysis, from which we develop a distributed algorithm to derive the system parameters for the opportunistic routing protocol. Extensive simulation results indicate the improved performance of our opportunistic routing protocol in terms of end-to-end delay, especially for CRNs with highly dynamic channel conditions.

Implementation of a calendar application based on SyD coordination links

System on devices (SyD) is a specification for a middleware to enable heterogeneous collections of information, databases, or devices (such as hand-held devices) to collaborate with each other. This paper illustrates the advantages of SyD by describing a prototype calendar of meetings application. This application highlights some of the technical merits of SyD by exploiting the use of coordination links. Based on the underlying event-and-trigger mechanism, these links allow automatic updates as well as real-time enforcements of global constraints and interdependencies, not available with existing calendar applications. Additionally, the calendar application illustrates coordination among heterogeneous devices and databases, formation and maintenance of dynamic groups, mobility support through proxies, and performance group transactions across independent data stores.

Optimally Scaling Permutation Routing on Reconfigurable Linear Arrays with Optical Buses

We present an optimal and scalable permutation routing algorithm for three reconfigurable models based on linear arrays that allow pipelining of information through an optical bus. Specifically, for any P?N, our algorithm routes any permutation of N elements on a P-processor model optimally in O(NP) steps. This algorithm extends naturally to one for routing h-relations optimally in O(h) steps. We also establish the equivalence of the three models: linear array with a reconfigurable pipelined bus system, linear pipelined bus, and pipelined optical bus. This implies an automatic translation of algorithms (without loss of speed or efficiency) among these models.

Tighter and Broader Complexity Results for Reconfigurable Models

A number of models allow processors to reconfigure their local connections to create and alter various bus configurations. This reconfiguration enables development of fast algorithms for fundamental problems, many in constant time. We investigate the ability of such models by relating time and processor bounded complexity classes defined for these models to each other and to those of more traditional models. In this work, (1) we tighten the relations for some of the models, placing them more precisely in relation to each other than was previously known (particularly, the Linear Reconfigurable Network and Directed Reconfigurable Network relative to circuit-defined classes), and (2) we include models (Fusing-Restricted Reconfigurable Mesh and Pipelined Reconfigurable Mesh) not previously considered.