This study was conducted to further research into the impact of student-group item parameter drift (SIPD) --referred to as subpopulation item parameter drift in previous research-- on ability estimates and proficiency classification accuracy when occurring in the discrimination parameter of a 2-PL item response theory (IRT) model. Using Monte…

Student growth percentiles (SGPs) have become a common means to measure and report on student academic growth for state education accountability, and some states have adopted SGP cutscores as a means of classifying student growth into categories like "high/medium/low" growth. It has therefore become important to understand properties of…

Since the phenomenal success of deep neural networks (DNNs) on image classification, the research community have been developing wider and deeper networks with complex components for a variety of visual understanding tasks. While such "heavy" models achieve excellent performance, they pose two main challenges: (1) the training requires a…

This study was conducted to analyze potential item parameter drift (IPD) impact on person ability estimates and classification accuracy when drift affects an examinee sub-group. Using a series of simulations, three factors were manipulated: (a) percentage of IPD items in the CAT exam, (b) percentage of examinees affected by IPD, and (c) item pool…

A primary purpose of cognitive diagnosis models (CDMs) is to classify examinees based on their attribute patterns. The Q-matrix (Tatsuoka, 1985), a common component of all CDMs, specifies the relationship between the set of required dichotomous attributes and the test items. Since a Q-matrix is often developed by content-knowledge experts and can…

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. Classification of imbalanced data is an…

The inverted index is the main data structure used by all the major search engines. Search engines build an inverted index on their collection to speed up query processing. As the size of the web grows, the length of the inverted list structures, which can easily grow to hundreds of MBs or even GBs for common terms (roughly linear in the size of…

A large body of literature exists on evolutionary computing, genetic algorithms, decision trees, codified knowledge, and knowledge management systems; however, the intersection of these computing topics has not been widely researched. Moving through the set of all possible solutions--or traversing the search space--at random exhibits no control…

Diagnostic classification models (DCMs) are structured latent class models widely discussed in the field of psychometrics. They model subjects' underlying attribute patterns and classify subjects into unobservable groups based on their mastery of attributes required to answer the items correctly. The effective implementation of DCMs depends…

The broad application domain of the work presented in this thesis is pattern classification with a focus on gesture recognition and 3D hand pose estimation. One of the main contributions of the proposed thesis is a novel method for 3D hand pose estimation using RGB-D. Hand pose estimation is formulated as a database retrieval problem. The proposed…

The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…

In my dissertation, I will present my research which contributes to solve the following three open problems from biomedical informatics: (1) Multi-task approaches for microarray classification; (2) Multi-label classification of gene and protein prediction from multi-source biological data; (3) Spatial scan for movement data. In microarray…

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack…

Today's Internet-based crime reporting systems make timely and anonymous crime reporting possible. However, these reports also result in a rapidly growing set of unstructured text files. Complicating the problem is that the information has not been filtered or guided in a detective-led interview resulting in much irrelevant information. To…