Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Major Sport Event Operational Planning Issues and Strategies: A Multi-Case Delphi Study

This study examined the operational planning, implementation and execution issues of major sport events, as well as the mitigation and management strategies used to address these issues, with the aim of determining best practices in sport event operational planning. The three Research Questions were: 1) What can previous major sport events provide to guide the operational management of future events? 2) What are the operational issues that arise in the planning and execution of a major sport event, how are they mitigated and what are the strategies used to deal with these issues? 3) What are the best practices for sport event operational planning and how can these practices aid future events? Data collection involved a modified Delphi technique that consisted of one round of in-depth interviews followed by two rounds of questionnaires. Both data collection and analysis were guided by an adaptation of the work of Parent, Rouillard & Leopkey (2011) with a focus on previously established issue and strategy categories. The results provided a list of Top 26 Prominent Issues and Top 17 Prominent Strategies with additional issue-strategy links that can be used to aid event managers producing future major sport events. The following issue categories emerged as having had the highest impact on previous major sport events that participants had managed: timing, funding and knowledge management. In addition, participants used strategies from the following categories most frequently: other, formalized agreements and communication.

Working Together: Librarian and Student Collaboration Through Active Learning in a Library Eclassroom

Active learning strategies based on several learning theories were incorporated during instruction sessions for second year Biological Sciences students. The instructional strategies described in this paper are based primarily on sociocultural and collaborative learning theory, with the goal being to expand the relatively small body of literature currently available that discusses the application of these learning theories to library instruction. The learning strategies employed successfully involved students in the learning process ensuring that the experiences were appropriate and effective. The researchers found that, as a result of these strategies (e.g. teaching moments based on the emerging needs of students) students’ interest in learning information literacy was increased and students interacted with information given to them as well as with their peers. Collaboration between the Librarians, Co-op Student and Senior Lab Instructor helped to enhance the learning experience for students and also revealed new aspects of the active learning experiences. The primary learning objective, which was to increase the students’ information skills in the Biological Sciences, was realized. The advantages of active learning were realized by both instructors and students. Advantages for students attained during these sessions include having their diverse learning styles addressed; increased interaction with and retention of information; increased responsibility for their own learning; the opportunity to value not only the instructors, but also themselves and their peers as sources of authority and knowledge; improved problem solving abilities; increased interest and opportunities for critical thinking, as a result of the actively exchanging information in a group. The primary advantage enjoyed by the instructors was the opportunity to collaborate with colleagues to reduce the preparation required to create effective library instruction sessions. Opportunities for further research were also discovered, including the degree to which “social loafing” plays a role in collaborative, active learning.