School of Electrical Engineering and Technology (SEET)
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School of Electrical Engineering and Technology (SEET)
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Item Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection(IEEE, 2020) Dogo, E. M.; Nwulu, N. I.; Twala, B.; Aigbavboa, C. O.Imbalanced class distribution and missing data are two common problems and occurrences in water quality anomaly detection domain. Learning algorithms in an imbalanced dataset can yield an overrated classification accuracy driven by a bias towards the majority class at the expense of the minority class. On the other hand, missing values in data can induce complexity in the learning classifiers during data analysis. These two problems pose substantial challenges to the performance of learning algorithms in real-life water quality anomaly detection problems. Hence, the need for them to be carefully considered and addressed to achieve better performance. In this paper, the performance of a range of several combinations of techniques to deal with imbalanced classes in the context of binary-imbalanced water quality anomaly detection problem and the presence of missing values is extensively compare. The methods considered include seven missing data and eight resampling methods, on ten different learning state-of-the-art classifiers taking into account diversity in their learning philosophies. The different classifiers are evaluated using stratified 5-fold cross-validation, based on three performance evaluation metrics namely accuracy, ROC-AUC and F1-measure. Further experiments are carried out on nineteen variants of homogeneous and heterogeneous ensemble techniques embedded with resampling and missing value strategies during their training phase as well as an optimized deep neural network model. The experimental results show an improvement in the performance of the learning classifiers, especially when dealing with the class imbalance problem (on the one hand) and the incomplete data problem (on the other hand). Furthermore, the neural network model exhibit superior performance when dealing with both problems.Item Sensed Outlier Detection for Water Monitoring Data and a Comparative Analysis of Quantization Error Using Kohonen Self-Organizing Maps(IEEE, 2018) Dogo, E. M.; Nwulu, N. I.; Twala, B. and Aigbavboa, C. O.Measurement values obtained from sensors deployed in the field are sometimes prone to deviation from known patterns of the sensed data which is referred to as outlier or anomalous readings. The reasons for this outlier may include noise, faulty sensor errors, environmental events and cyber-attack on the sensor network, resulting in faulty and missing data that greatly affects quality of the raw data and its subsequent analysis. This paper employs the Self-Organizing Maps (SOM) algorithm to visualise and interpret clusters of sensed data obtained from fresh water monitoring sites, with patterns of similar expressions in a graphical form. With the aim of detecting potential anomalous sensed data, so that they could be investigated and possibly removed to guarantee the quality of the overall dataset. Furthermore, a comparative study of the effects of four different well known neighborhood functions (gaussian, bubble, triangle and mexican hat) with varying neighborhood radius (σ) and learning rate (η) values on Quantization Error (QE) metric was conducted. From the experiment conducted a 3.45% potentially anomalous sensed data were discovered from the entire dataset, in addition, our initial finding suggests a very insignificant variation of the QE based on our dataset and the experiments conducted.