The Order of Canada Investiture Ceremony Program (Selection of Pages)

A selection of pages from the program for the Order of Canada Investiture Ceremony in 2003 when Dorothy Wetherald Rungeling was a recipient.

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.

County of Welland estimate of work done on section no.1 of the tap drain at Marshville by Edward Henderson

County of Welland estimate of work done on section no.1 of the tap drain at Marshville by Edward Henderson, signed by S.D. Woodruff. Estimate no.1, June, 1856.

County of Welland estimate of work done on constructing a bridge across the tap drain at Marshville by Alexander Lattimer

County of Welland estimate of work done on constructing a bridge across the tap drain at Marshville by Alexander Lattimer, signed by S.D. Woodruff. Estimate no.1, June, 1856.

County of Welland estimate (copy) of work done on sections 2 and 3 of the tap drain at Marshville by George Stanford

County of Welland estimate (copy) of work done on sections 2 and 3 of the tap drain at Marshville by George Stanford, signed by S.D. Woodruff. Estimate no.1, July, 1856.

County of Welland estimate (copy) of work done on section no. 5 of the tap drain at Marshville by Andrew Mains

County of Welland estimate (copy) of work done on section no. 5 of the tap drain at Marshville by Andrew Mains, signed by S.D. Woodruff. Estimate no.1, Sept., 1856.

County of Welland estimate (copy) of work done on section no. 4 of the tap drain at Marshville by Brown and Hershall

County of Welland estimate (copy) of work done on section no. 4 of the tap drain at Marshville by Brown and Hershall, signed by S.D. Woodruff. Estimate no.1, Sept., 1856.

County of Welland estimate (copy) of work done on sections 2 and 3 of the tap drain at Marshville by George Stanford

County of Welland estimate (copy) of work done on sections 2 and 3 of the tap drain at Marshville by George Stanford, signed by S.D. Woodruff. Estimate no.2, Oct., 1856.

County of Welland estimate (copy) of work done on section no. 4 of the tap drain at Marshville by Brown and Hershall,

County of Welland estimate (copy) of work done on section no. 4 of the tap drain at Marshville by Brown and Hershall, signed by S.D. Woodruff. Estimate no.2, Oct., 1856.

County of Welland estimate of work done on section no.1 and cleaning below the culvert of the tap drain at Marshville by Edward Henderson

County of Welland estimate of work done on section no.1 and cleaning below the culvert of the tap drain at Marshville by Edward Henderson, signed by S.D. Woodruff. Estimate no.2, Oct., 1856.

Abstracts given to Andrew Mains for ditching done in the tap drain of the marsh lands

Abstracts given to Andrew Mains for ditching done in the tap drain of the marsh lands, Oct. 1856.

Dealing with Moral Hazard and Adverse Selection Simultaneously

Although Insurers Face Adverse Selection and Moral Hazard When They Set Insurance Contracts, These Two Types of Asymmetrical Information Have Been Given Separate Treatments Sofar in the Economic Literature. This Paper Is a First Attempt to Integrate Both Problems Into a Single Model. We Show How It Is Possible to Use Time in Order to Achieve a First-Best Allocation of Risks When Both Problems Are Present Simultaneously.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

SCaFoS: a tool for Selection, Concatenation and Fusion of Sequences for phylogenomics

Affiliation: Département de Biochimie, Université de Montréal