The purpose of this doctoral thesis research study was first to investigate the needs for a faculty development program in Middle East Technical University (METU). Later, in the second phase, models that explained the linear structural relationships among factors that might be influential on faculty&amp / #146 / s perceived competencies about the skills necessary for the instructional practices, personal, professional and organizational developments were proposed and compared. In this study, a questionnaire considering different aspects of faculty developments were sent to all of the academicians in METU. After collecting data from faculty members and research assistants, they were analyzed both descriptively and using principal component factor analysis. Based on the results of factor analysis, linear structural relations models fitting the data were generated through LISREL-SIMPLIS computer program runs. The descriptive results indicated that there was a feeling for need to improve the faculty&#039 / s self-proficiency in different instructional issues. On the other hand, both descriptive results and LISREL modeling results indicated that faculty members and research assistants show different characteristics based on their needs and factors affecting their self-proficiencies. These aspects will lead us to prepare different faculty development programs based on their needs and priorities. The result for both faculty members and research assistants showed that in a faculty, instructional self-proficiency cannot be considered as a single absolute parameter. Rather, it should be considered as several interrelated parameters connected to different aspects of faculty&#039 / s proficiencies.

abstract: This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages. / Dissertation/Thesis / Ph.D. Industrial Engineering 2011