Ant system-based feature set partitioning algorithm for K-NN and LDA ensembles construction

Combination of several classifiers has been very useful in improving the prediction accuracy and in most situations multiple classifiers perform better than single classifier.However not all combining approaches are successful at producing multiple classifiers with good classification accuracy because there is no standard resolution in constructing diverse and accurate classifier ensemble.This paper proposes ant system-based feature set partitioning algorithm in constructing k-nearest neighbor (k-NN) and linear discriminant analysis (LDA) ensembles. Experiments were performed on several University California, Irvine datasets to test the performance of the proposed algorithm.Experimental results showed that the proposed algorithm has successfully constructed better classifier ensemble for k-NN and LDA

On Tackling Real-Life Optimization Problems

Most real-world applications are concerned with minimizing or maximizing some quantity so as to enhance some result. This emphasizes the importance of optimization and subsequently the significance of the optimization methods that are able to tackle these real-life optimization problems. There are a number of practical reasons for which traditional optimization and exhaustive algorithms cannot deal with a variety of these real-life optimization applications although there are numerous optimization problems that can benefit from applying these traditional optimization algorithms to handle them. Therefore, their is a need for propsong new optimization algorithms (such as nature inspired optimization methods) and optimize the capabilities of the existing ones (such as hybridization and parallelization) as well. This paper investigates the most recent optimization directions for dealing with the real-life optimization problems with an application to one of the most common and important optimization problems in a variety of financial fields and other fields which is the portfolio optimization problem since it is considered one of the most crucial problems in the modern financial management and has a variety of applications such as asset management and building strategic asset allocation. The computational results were got utilizing benchmark data from the OR library with the use of modern optimization algorithms. In addition, the article highlights the differences and similarities among the utilized optimization methods. In addition, recent advancements to the utilized optimization methods are highlighted

Reservoir gate opening classification using multiple classifier system with ant system-based feature decomposition

Classification of reservoir gate opening (RGO) is an important task in flood management.Reservoir water level has been used to determine the number of gates to be opened when flood is imminent to prevent disaster.Predicting the number of gates to be opened is crucial to avoid any disaster. Multiple classifier system has been shown to provide better classification accuracy as compared to single classifier system.However, there is no guideline on the number of classifiers to be combined and no measurement was proposed to measure the compactness of the classifiers.This study proposes an ant system-based feature decomposition approach to develop a multiple classifier ensemble for classification of RGO.Experiments have been conducted using the k-nearest neighbour, decision tree, nearest mean classifier and linear discriminant analysis as base classifier, and performance of ant system has been compared with random subspace method.Based on the results, it can be concluded that the multiple classifier with ant system-based feature decomposition produced better classification accuracy than random subspace method. Best classification results were obtained when multiple decision tree is constructed to make predictions of RGO with an average accuracy of 89.17%. This method is expected to be useful to apply for RGO classification and future work can be done to include rainfall precipitation besides reservoir water level

An improved multiple classifier combination scheme for pattern classification

Combining multiple classifiers are considered as a new direction in the pattern recognition to improve classification performance. The main problem of multiple classifier combination is that there is no standard guideline for constructing an accurate and diverse classifier ensemble. This is due to the difficulty in identifying the number of homogeneous classifiers and how to combine the classifier outputs. The most commonly used ensemble method is the random strategy while the majority voting technique is used as the combiner. However, the random strategy cannot determine the number of classifiers and the majority voting technique does not consider the strength of each classifier, thus resulting in low classification accuracy. In this study, an improved multiple classifier combination scheme is proposed. The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. A compactness measure is introduced as a parameter in constructing an accurate and diverse classifier ensemble. A weighted voting technique is used to combine the classifier outputs by considering the strength of the classifiers prior to voting. Experiments were performed using four base classifiers, which are Nearest Mean Classifier (NMC), Naive Bayes Classifier (NBC), k-Nearest Neighbour (k-NN) and Linear Discriminant Analysis (LDA) on benchmark datasets, to test the credibility of the proposed multiple classifier combination scheme. The average classification accuracy of the homogeneous NMC, NBC, k-NN and LDA ensembles are 97.91%, 98.06%, 98.09% and 98.12% respectively. The accuracies are higher than those obtained through the use of other approaches in developing multiple classifier combination. The proposed multiple classifier combination scheme will help to develop other multiple classifier combination for pattern recognition and classification

Performance analysis for network coding using ant colony routing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The aim of this thesis is to conduct performance investigation of a combined system of Network Coding (NC) technique with Ant-Colony (ACO) routing protocol. This research analyses the impact of several workload characteristics, on system performance. Network coding is a significant key development of information transmission and processing. Network coding enhances the performance of multicast by employing encoding operations at intermediate nodes. Two steps should realize while using network coding in multicast communication: determining appropriate transmission paths from source to multi-receivers and using the suitable coding scheme. Intermediate nodes would combine several packets and relay them as a single packet. Although network coding can make a network achieve the maximum multicast rate, it always brings additional overheads. It is necessary to minimize unneeded overhead by using an optimization technique. On other hand, Ant Colony Optimization can be transformed into useful technique that seeks imitate the ant’s behaviour in finding the shortest path to its destination using quantities of pheromone that is left by former ants as guidance, so by using the same concept of the communication network environment, shorter paths can be formulated. The simulation results show that the resultant system considerably improves the performance of the network, by combining Ant Colony Optimization with network coding. 25% improvement in the bandwidth consumption can be achieved in comparison with conventional routing protocols. Additionally simulation results indicate that the proposed algorithm can decrease the computation time of system by a factor of 20%

On the use of primal and dual knowledge in randomized constructive optimization algorithms

Many approximate heuristics for optimization are either based on neighborhood search or on the construction of solutions. Examples for the latter ones include ant colony optimization and greedy randomized adaptive search procedures. These techniques generally construct solutions probabilistically by sampling a probability distribution over the search space. Solution constructions are generally independent from each other. Recent algorithmic variants include two important features that are inspired by deterministic branch & bound derivatives such as beam search: the use of bounds for evaluating partial solutions, and the parallel and non-independent construction of solutions. In this paper we first give a theoretical reason of why these variants have the potential to improve over standard algorithms. Second, we confirm our theoretical findings by means of practical examples. Our results for the open shop scheduling problem clearly demonstrate the potential of using parallel and non-independent solution constructions.Postprint (published version

Performance analysis for network coding using ant colony routing

The aim of this thesis is to conduct performance investigation of a combined system of Network Coding (NC) technique with Ant-Colony (ACO) routing protocol. This research analyses the impact of several workload characteristics, on system performance. Network coding is a significant key development of information transmission and processing. Network coding enhances the performance of multicast by employing encoding operations at intermediate nodes. Two steps should realize while using network coding in multicast communication: determining appropriate transmission paths from source to multi-receivers and using the suitable coding scheme. Intermediate nodes would combine several packets and relay them as a single packet. Although network coding can make a network achieve the maximum multicast rate, it always brings additional overheads. It is necessary to minimize unneeded overhead by using an optimization technique. On other hand, Ant Colony Optimization can be transformed into useful technique that seeks imitate the ant’s behaviour in finding the shortest path to its destination using quantities of pheromone that is left by former ants as guidance, so by using the same concept of the communication network environment, shorter paths can be formulated. The simulation results show that the resultant system considerably improves the performance of the network, by combining Ant Colony Optimization with network coding. 25% improvement in the bandwidth consumption can be achieved in comparison with conventional routing protocols. Additionally simulation results indicate that the proposed algorithm can decrease the computation time of system by a factor of 20%.EThOS - Electronic Theses Online ServiceGBUnited Kingdo