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.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

'Art is the New Steel': Marketing Creative Urbanism in Twenty-First Century Hamilton, Ontario

As a major manufacturing hub in southern Ontario, Hamilton enjoyed considerable economic stability during the twentieth century. However, like most industrial-based cities, Hamilton’s role as a North American manufacturing producer has faded since the 1970’s. This has resulted in dramatic socio-economic impacts, most of which are centered on the inner city. There have been many attempts to revive the core. This includes Hamilton’s most recent urban renewal plans, based upon the principles of Richard Florida’s creative city hypothesis and Ontario’s Places to Grow Act (2005). Common throughout all of Hamilton’s urban renewal initiatives has been the role of the local press. In this thesis I conduct a discourse analysis of media based knowledge production. I show that the local press reproduces creative city discourses as local truths to substantiate and validate a revanchist political agenda. By choosing to celebrate the creative class culture, the local press fails to question its repercussions

Investigating Changes in Parent Knowledge about Obsessive Compulsive Behaviour Following Group Function-Based Cognitive Behavioural Therapy for Children with High Functioning Autism

This study investigated improvements in parent knowledge of effective intervention strategies following participation in a group function-based CBT treatment (GFbCBT) package for children with comorbid OCD and ASD. Nineteen parents of children ages 7-12 years with High Functioning Autism (HFA) participated in the 9-week treatment program. Key components of treatment included psychoeducation and mapping, cognitive-behavioural skills training, function-based interventions and exposure and response prevention (ERP). Treatment sessions also included direct parent education, which followed a behavioural skills training model (Miltenberger, 2008). Parent knowledge (N = 19) was measured pre and post treatment using a vignette about a child demonstrating obsessive-compulsive behaviour. Results of a one-tailed pairwise t-test indicated statistically significant changes (p=.036) in overall parent knowledge following participation in treatment. Statistically significant changes were also found in parents’ ability to generate ERP and function-based intervention strategies. These results provide preliminary evidence that parents benefit from active involvement in the GFbCBT treatment package.

Evaluating Knowledge of a Function-based Cognitive-Behavioural Treatment for Obsessive Compulsive Behaviour in Autism Spectrum Disorder

Children with Autism Spectrum Disorder (ASD) have restricted and repetitive behaviours (RRBs) which may be similar to obsessions and compulsions in Obsessive Compulsive Disorder (OCD). These behaviours can be intrusive and interfere in the lives of the child and their family. Preliminary studies have shown success in using adapted Cognitive Behavioural Therapy (CBT) to treat these behaviors in children with high functioning ASD. Using a hypothetical vignette, this thesis attempted to examine procedural knowledge that the children and their parents gained while participating in a CBT treatment that was evaluated in a Randomized Controlled Trial. For both parents and children, there was a significant increase in number of strategies generated from pre to post-treatment. Further, children in the experimental group generated significantly more strategies than the treatment as usual (TAU) group post-intervention. There was no significant correlation between number of strategies generated and the child’s treatment success, age, or IQ.