This study investigates the impact of increasing levels of simulated class noise on software quality classification. Class noise was injected into seven software engineering measurement datasets, and the performance of three learners, random forests, C4.5, and Naive Bayes, was analyzed. The random forest classifier was utilized for this study because of its strong performance relative to well-known and commonly-used classifiers such as C4.5 and Naive Bayes. Further, relatively little prior research in software quality classification has considered the random forest classifier. The experimental factors considered in this study were the level of class noise and the percent of minority instances injected with noise. The empirical results demonstrate that the random forest obtained the best and most consistent classification performance in all experiments.

Erroneous attribute values can significantly impact learning from otherwise valuable data. The learning impact can be exacerbated by the class imbalanced training data. We investigate and compare the overall learning impact of sampling such data by using four distinct performance metrics suitable for models built from binary class imbalanced data. Seven relatively free of noise, class imbalanced software engineering measurement datasets were used. A novel noise injection procedure was applied to these datasets. We injected domain realistic noise into the independent and dependent (class) attributes of randomly selected instances to simulate lower quality measurement data. Seven well known data sampling techniques with the benchmark decision-tree learner C4.5 were used. No other related studies were found that have comprehensively investigated learning by sampling low quality binary class imbalanced data containing both independent and dependent corrupted attributes. Two sampling techniques (random undersampling and Wilson's editing) with better and more robust learning performances were identified. In contrast, all metrics concurred on the identification of the worst performing sampling technique (cluster-based oversampling).

A practical problem in data mining and machine learning is the limited availability of data. For example, in a binary classification problem it is often the case that examples of one class are abundant, while examples of the other class are in short supply. Examples from one class, typically the positive class, can be limited due to the financial cost or time required to collect these examples. This work presents a comprehensive empirical study of learning when examples from one class are extremely rare, but examples of the other class(es) are plentiful. Specifically, we address the issue of how many examples from the abundant class should be used when training a classifier on data where one class is very rare. Nearly one million classifiers were built and evaluated to generate the results presented in this work. Our results demonstrate that the often used 'even distribution' is not optimal when dealing with such rare events.

This paper addresses the problems associated with missions using multiple cooperating autonomous underwater vehicles. In such missions, inter-vehicle communication is an important requirement upon which coordination strategy and intelligence decision making functions can be built. This paper presents briefly our current modeling and simulation work on characterizing the vehicle communication performance, together with some of the preliminary results. Even with much simplification of scenario modeling, simulation results exhibit intricate communication characteristics, and they can vary significantly as a function of mission requirements and scenarios.