Ashfaqur Rahman

Novel Layered Clustering-Based Approach for Generating Ensemble of Classifiers

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.

Cluster-Oriented Ensemble Classifier: Impact of Multicluster Characterization on Ensemble Classifier Learning

This paper presents a novel cluster-oriented ensemble classifier. The proposed ensemble classifier is based on original concepts such as learning of cluster boundaries by the base classifiers and mapping of cluster confidences to class decision using a fusion classifier. The categorized data set is characterized into multiple clusters and fed to a number of distinctive base classifiers. The base classifiers learn cluster boundaries and produce cluster confidence vectors. A second level fusion classifier combines the cluster confidences and maps to class decisions. The proposed ensemble classifier modifies the learning domain for the base classifiers and facilitates efficient learning. The proposed approach is evaluated on benchmark data sets from UCI machine learning repository to identify the impact of multicluster boundaries on classifier learning and classification accuracy. The experimental results and two-tailed sign test demonstrate the superiority of the proposed cluster-oriented ensemble classifier over existing ensemble classifiers published in the literature.

Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm

In this paper, we propose a novel cluster oriented ensemble classifier generation method and a Genetic Algorithm based approach to optimize the parameters. In the proposed method the data set is partitioned into a variable number of clusters at different layers. Base classifiers are trained on the clusters at different layers. Due to the variability of the number of clusters at different layers, the cluster compositions in one layer are different from that in another layer. Due to this difference in cluster contents, the base classifiers trained at different layers are diverse among each other. A test pattern is classified by the base classifier of the nearest cluster at each layer and the decisions from different layers are fused using majority voting. The accuracy of the proposed method depends on the number of layers and the number of clusters at the corresponding layer. A Genetic Algorithm based search is incorporated to obtain the optimal number of layers and clusters. The Genetic Algorithm is evaluated under three different objective functions: optimizing (i) accuracy, (ii) diversity, and (iii) accuracyxdiversity. We have conducted a number of experiments to evaluate the effectiveness of the different objective functions.

Cluster-based ensemble of classifiers

This paper presents cluster-based ensemble classifier – an approach toward generating ensemble of classifiers using multiple clusters within classified data. Clustering is incorporated to partition data set into multiple clusters of highly correlated data that are difficult to separate otherwise and different base classifiers are used to learn class boundaries within the clusters. As the different base classifiers engage on different difficult-to-classify subsets of the data, the learning of the base classifiers is more focussed and accurate. A selection rather than fusion approach achieves the final verdict on patterns of unknown classes. The impact of clustering on the learning parameters and accuracy of a number of learning algorithms including neural network, support vector machine, decision tree and k-NN classifier is investigated. A number of benchmark data sets from the UCI machine learning repository were used to evaluate the cluster-based ensemble classifier and the experimental results demonstrate its superiority over bagging and boosting.

A Novel Machine Learning Approach Toward Quality Assessment of Sensor Data

A novel machine learning approach to assess the quality of sensor data using an ensemble classification framework is presented in this paper. The quality of sensor data is indicated by discrete quality flags that indicate the level of uncertainty associated with a sensor reading. Depending on the domain and the problem under consideration, the level of uncertainty is different and thus unsupervised methods like outlier detection fails to match the expectation. The quality flags are normally assigned by domain experts. Considering the volume of sensor data, manual assignment is a laborious task and subject to human error. Given a representative set of labelled data, a supervised classification approach is thus a feasible alternative. The nature of sensor data, however, poses some challenges to the classification task. Data of dubious quality exists in such data sets with very small frequency leading to the class imbalance problem. We thus adopt a cluster oriented sampling approach to address the imbalance issue. In addition, it is beneficial to train multiple classifiers to improve the overall classification accuracy. We thus produce multiple under-sampled training sets using cluster oriented sampling and train base classifiers on each of them. Decisions produced by the base classifiers are fused into a single decision using majority voting. We have evaluated the proposed ensemble classification framework by assessing the quality of marine sensor data obtained from sensors situated at Sullivans Cove, Hobart, Australia. Experimental results reveal that the proposed framework agrees with expert judgement with high accuracy and achieves superior classification performance than other state-of-the-art approaches.

Design and Evaluation of a Metropolitan Air Pollution Sensing System

Urban air pollution is believed to be a major contributor to premature deaths and chronic illnesses worldwide. Current systems for urban air pollution monitoring rely on static sites with low spatial resolution, and moreover, lack the means to estimate exposures for (potentially mobile) individuals in order to make medical inferences. This paper describes the design and evaluation of a low-cost participatory sensing system called HazeWatch that uses a combination of portable mobile sensor units, smart-phones, cloud computing, and mobile apps to measure, model, and personalize air pollution information for individuals. Our contributions are three-fold: we architect, prototype, and compare multiple hardware devices and software applications for collecting urban air pollution data with high spatial density in real-time; we develop web-based tools and mobile apps for the visualization and estimation of air pollution exposure customized to individuals; and we conduct field trials to validate our system and demonstrate that it yields much more accurate exposure estimates than current systems. We believe our system can increase user engagement in exposure management, and better inform medical studies linking air pollution with human health.

Predicting shellfish farm closures with class balancing methods

Real-time environmental monitoring can provide vital situational awareness for effective management of natural resources. Effective operation of Shellfish farms depends on environmental conditions. In this paper we propose a supervised learning approach to predict the farm closures. This is a binary classification problem where farm closure is a function of environmental variables. A problem with this classification approach is that farm closure events occur with small frequency leading to class imbalance problem. Straightforward learning techniques tend to favour the majority class; in this case continually predicting no event. We present a new ensemble class balancing algorithm based on random undersampling to resolve this problem. Experimental results show that the class balancing ensemble performs better than individual and other state of art ensemble classifiers. We have also obtained an understanding of the importance of relevant environmental variables for shellfish farm closure. We have utilized feature ranking algorithms in this regard.

Effect of ensemble classifier composition on offline cursive character recognition

In this paper we present novel ensemble classifier architectures and investigate their influence for offline cursive character recognition. Cursive characters are represented by feature sets that portray different aspects of character images for recognition purposes. The recognition accuracy can be improved by training ensemble of classifiers on the feature sets. Given the feature sets and the base classifiers, we have developed multiple ensemble classifier compositions under four architectures. The first three architectures are based on the use of multiple feature sets whereas the fourth architecture is based on the use of a unique feature set. Type-1 architecture is composed of homogeneous base classifiers and Type-2 architecture is constructed using heterogeneous base classifiers. Type-3 architecture is based on hierarchical fusion of decisions. In Type-4 architecture a unique feature set is learned by a set of homogeneous base classifiers with different learning parameters. The experimental results demonstrate that the recognition accuracy achieved using the proposed ensemble classifier (with best composition of base classifiers and feature sets) is better than the existing recognition accuracies for offline cursive character recognition.

Non-uniform layered clustering for ensemble classifier generation and optimality

In this paper we present an approach to generate ensemble of classifiers using non-uniform layered clustering. In the proposed approach the dataset is partitioned into variable number of clusters at different layers. A set of base classifiers is trained on the clusters at different layers. The decision on a pattern at each layer is obtained from the classifier trained on the nearest cluster and the decisions from the different layers are fused using majority voting to obtain the final verdict. The proposed approach provides a mechanism to obtain the optimal number of layers and clusters using a Genetic Algorithm. Clustering identifies difficult-to-classify patterns and layered non-uniform clustering approach brings in diversity among the base classifiers at different layers. The proposed method performs relatively better than the other state-of-art ensemble classifier generation methods as evidenced from the experimental results.

Personalising pollution exposure estimates using wearable activity sensors

In recent years several research groups, including ours, have demonstrated participatory systems that use wearable or vehicle-mounted portable units coupled with smartphones to crowdsource urban air pollution data from lay users. These systems have shown remarkable improvement in spatial granularity over government-operated monitoring systems, leading to better mapping and understanding of urban air pollution, at relatively low cost. In this paper we extend the paradigm to personalize the consumption of data by individuals. Specifically, we combine the pollution concentrations obtained from participatory systems with the individuals on-body activity monitors to estimate the personal inhalation dosage of air pollution. We show that the individuals activity, such as jogging, cycling, or driving, impacts their dosage, and develop an app that gives them this personalised information. Our system is a step towards enabling medical inferencing of the impact of air pollution on individual health.