Abdul Rauf Baig

Opposition based initialization in particle swarm optimization (O-PSO)

Particle Swarm Optimization, a population based optimization technique has been used in wide number of application areas to solve optimization problems. This paper presents a new algorithm for initialization of population in standard PSO called Opposition based Particle Swarm Optimization (O-PSO). The performance of proposed initialization algorithm is compared with the existing PSO variants on several benchmark functions and the experimental results reveal that O-PSO outperforms existing approaches to a large extent.

A novel ant colony optimization based single path hierarchical classification algorithm for predicting gene ontology

There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels. Unfortunately, less attention is given to the genre of classification problems where the classes are organized as a structured hierarchy; such as protein function prediction (target area in this work), test scores, gene ontology, web page categorization, text categorization etc. The structured hierarchy is usually represented as a tree or a directed acyclic graph (DAG) where there exist IS-A relationship among the class labels. Class labels at upper level of the hierarchy are more abstract and easy to predict whereas class labels at deeper level are most specific and challenging for correct prediction. It is helpful to consider this class hierarchy for designing a hypothesis that can handle the tradeoff between prediction accuracy and prediction specificity. In this paper, a novel ant colony optimization (ACO) based single path hierarchical classification algorithm is proposed that incorporates the given class hierarchy during its learning phase. The algorithm produces IF-THEN ordered rule list and thus offer comprehensible classification model. Detailed discussion on the architecture and design of the proposed technique is provided which is followed by the empirical evaluation on six ion-channels data sets (related to protein function prediction) and two publicly available data sets. The performance of the algorithm is encouraging as compared to the existing methods based on the statistically significant Students t-test (keeping in view, prediction accuracy and specificity) and thus confirm the promising ability of the proposed technique for hierarchical classification task.

Improved Opposition-Based PSO for Feedforward Neural Network Training

In this study we present an improved opposition- based PSO and apply it to feedforward neural network training. The improved opposition-based PSO utilizes opposition-based initialization, opposition-based generation jumping and opposition-based velocity calculation. The opposition-based PSO is first tested on some unimodal and multimodal problems and its performance is compared with standard PSO. We then test the performance of the improved opposition-based PSO for training feedforward neural network and also present a comparison with standard PSO.

Mine Detection and Route Planning in Military Warfare using Multi Agent System

One of the most promising uses for multi agent systems is the searching for items or resources in unknown environments. The use of multi agent systems to locate unexploded ordinance proves to be an excellent example of one such application. This research explores the possibility of a hybrid architecture that implements mine detection, obstacle avoidance and route planning with a group of autonomous agents with coordination capabilities. Groups of inter cooperating multi agents working towards a common goal have the potential to perform a task faster and with an increased level of efficiency then the same number of agents acting in an independent manner. This coordination framework will address the issues involved during such unknown exploration

A correlation-based ant miner for classification rule discovery

In recent years, a few sequential covering algorithms for classification rule discovery based on the ant colony optimization meta-heuristic (ACO) have been proposed. This paper proposes a new ACO-based classification algorithm called AntMiner-C. Its main feature is a heuristic function based on the correlation among the attributes. Other highlights include the manner in which class labels are assigned to the rules prior to their discovery, a strategy for dynamically stopping the addition of terms in a rule’s antecedent part, and a strategy for pruning redundant rules from the rule set. We study the performance of our proposed approach for twelve commonly used data sets and compare it with the original AntMiner algorithm, decision tree builder C4.5, Ripper, logistic regression technique, and a SVM. Experimental results show that the accuracy rate obtained by AntMiner-C is better than that of the compared algorithms. However, the average number of rules and average terms per rule are higher.

Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization Based Classifiers

The primary objective of this research is to propose and investigate a novel ant colony optimization-based classification rule discovery algorithm and its variants. The main feature of this algorithm is a new heuristic function based on the correlation between attributes of a dataset. Several aspects and parameters of the proposed algorithm are investigated by experimentation on a number of benchmark datasets. We study the performance of our proposed approach and compare it with several state-of-the art commonly used classification algorithms. Experimental results indicate that the proposed approach builds more accurate models than the compared algorithms. The high accuracy supplemented by the comprehensibility of the discovered rule sets is the main advantage of this method.

Two-stage learning for multi-class classification using genetic programming

Abstract This paper introduces a two-stage strategy for multi-class classification problems. The proposed technique is an advancement of tradition binary decomposition method. In the first stage, the classifiers are trained for each class versus the remaining classes. A modified fitness value is used to select good discriminators for the imbalanced data. In the second stage, the classifiers are integrated and treated as a single chromosome that can classify any of the classes from the dataset. A population of such classifier-chromosomes is created from good classifiers (for individual classes) of the first phase. This population is evolved further, with a fitness that combines accuracy and conflicts. The proposed method encourages the classifier combination with good discrimination among all classes and less conflicts. The two-stage learning has been tested on several benchmark datasets and results are found encouraging.

Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games

Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developers task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for generating new and entertaining board games, provided an initial search space is given to the evolutionary algorithm.

HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach

In this paper we present a novel hybrid (array-based layout and vertical bitmap layout) database representation approach for mining complete maximal frequent itemset (MFI) on sparse and large datasets. Our work is novel in terms of scalability, item search order and two horizontal and vertical projection techniques. We also present a maximal algorithm using this hybrid database representation approach. Different experimental results on real and sparse benchmark datasets show that our approach is better than previous state of art maximal algorithms

Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.