A family of measures for best top-n class-selective decision rules

International audienceWhen classes strongly overlap in the feature space, or when some classes are not known in advance, the performance of a classifier heavily decreases. To overcome this problem, the reject option has been introduced. It simply consists in withdrawing the decision, and let another classifier, or an expert, take the decision whenever exclusively classifying is not reliable enough. The classification problem is then a matter of class-selection, from none to all classes. In this paper, we propose a family of measures suitable to define such decision rules. It is based on a new family of operators that are able to detect blocks of similar values within a set of numbers in the unit interval, the soft labels of an incoming pattern to be classified, using a single threshold. Experiments on synthetic and real datasets available in the public domain show the efficiency of our approach

Race, Income, and College in 25 Years: The Continuing Legacy of Segregation and Discrimination

The rate at which racial gaps in pre-collegiate academic achievement can plausibly be expected to erode is a matter of great interest and much uncertainty. In her opinion in Grutter v. Bollinger, Supreme Court Justice O'Connor took a firm stand: "We expect that 25 years from now, the use of racial preferences will no longer be necessary . . ." We evaluate the plausibility of Justice O'Connor's forecast, by projecting the racial composition and SAT distribution of the elite college applicant pool 25 years from now. We focus on two important margins: First, changes in the black-white relative distribution of income, and second, narrowing of the test score gap between black and white students within family income groups. Other things equal, progress on each margin can be expected to reduce the racial gap in qualifications among students pursuing admission to the most selective colleges. Under plausible assumptions, however, projected economic progress will not yield nearly as much racial diversity as is currently obtained with race-sensitive admissions. Simulations that assume additional increases in black students' test scores, beyond those deriving from changes in family income, yield more optimistic estimates. In this scenario, race-blind rules approach the black representation among admitted students seen today at moderately selective institutions, but continue to fall short at the most selective schools. Maintaining a critical mass of African American students at the most selective institutions would require policies at the elementary and secondary levels or changes in parenting practices that deliver unprecedented success in narrowing the test score gap in the next quarter century.

Race, Income and College in 25 Years: The Continuing Legacy of Segregation and Discrimination

The rate at which racial gaps in pre-collegiate academic achievement can plausibly be expected to erode is a matter of great interest and much uncertainty. In her opinion in Grutter v. Bollinger, Supreme Court Justice O’Connor took a firm stand: “We expect that 25 years from now, the use of racial preferences will no longer be necessary . . .” We evaluate the plausibility of Justice O’Connor’s forecast, by projecting the racial composition and SAT distribution of the elite college applicant pool 25 years from now. We focus on two important margins: First, changes in the black-white relative distribution of income, and second, narrowing of the test score gap between black and white students within family income groups. Other things equal, progress on each margin can be expected to reduce the racial gap in qualifications among students pursuing admission to the most selective colleges. Under plausible assumptions, however, projected economic progress will not yield nearly as much racial diversity as is currently obtained with race-sensitive admissions. Simulations that assume additional increases in black students’ test scores, beyond those deriving from changes in family income, yield more optimistic estimates. In this scenario, race-blind rules approach the black representation among admitted students seen today at moderately selective institutions, but continue to fall short at the most selective schools. Maintaining a critical mass of African American students at the most selective institutions would require policies at the elementary and secondary levels or changes in parenting practices that deliver unprecedented success in narrowing the test score gap in the next quarter century.

k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier -- classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data. This paper is the second edition of a paper previously published as a technical report. Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN

College and University Ranking Systems: Global Perspectives and American Challenges

Examines how higher education ranking systems function, how other countries use ranking systems, and the impact of college rankings in the United States on student access, choice, and opportunity

Leadership in Charter Schools A Case Study of Blackstone Valley Prep Mayoral Academy

This case study seeks to identify the leadership practices in a high performing charter school in order to determine specific practices that are effective, especially when educating minority and underprivileged students. This study is partnered with Blackstone Valley Prep Mayoral Academy (BVP), a mayoral charter school system consisting of six schools grades k-12. BVP is performing significantly statistically better than neighboring districts as well as Rhode Island schools as a whole. This research explores the leadership approaches and practices used to drive the success achieved by BVP. The data collected for this study derives from interviews with school administrators, faculty, and staff, observations of Board and Cabinet meetings, as well as various scholarly sources. The results include a strong focus on Autonomy and Best Practices, a People oriented approach, Learning by doing and seeing, and Managing change and controversy

Early Admission at Selective Colleges

Early admissions is widely used by selective colleges and universities. We identify some basic facts about early admissions policies, including the admissions advantage enjoyed by early applicants and patterns in application behavior, and propose a game theoretic model that matches these facts. The key feature of the model is that colleges want to admit students who are enthusiastic about attending, and early admissions programs give students an opportunity to signal this enthusiasm.Game Theory, Early Admission, Education

Educational policy, policy appropriation and Grameen Bank higher education financial aid policy process

The paper talks about higher educational polices and their process of policy appropriations, policy as practices, policy as symbolic, policy as rituals, policy as myths, policy backward- mapping and policy-forward mapping, multi-stage policy implementation process, street-bureaucrats planners, and policy reform process. It critically looks at pros-and-corns of different educational policy theories and their applications in education, and the higher education student financial aid different policies, strategies and products and their impact on the college students. The paper also narrates the higher educational policies and methods of need-based, merit-based, means-test-based grants allocation and loan disbursement and their impact on student academic achievements. Moreover, it discusses the policy process model that has both agendas and multiple streams that consider looking at policy designing problems, solutions of the problems and their usefulness to SES students. Additionally, the paper narrates the Grameen Bank higher education student loan policy making process, although there is no higher education student financial aid services are not exist in Bangladesh. Literature reviews, conversations with higher education students, contextual analysis, and the author personal working experience incorporate here. The study finds for policy improvement, policy analysis is vital because policy analysis can explores usefulness of the policy for public well being and for effectiveness of the policy appropriation.Center for Social Economy Learning and Workplace, University of Toronto. -- York Center for Asia Research, York University. -- Indiana University Bloomington

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

The GIST of Concepts

A unified general theory of human concept learning based on the idea that humans detect invariance patterns in categorical stimuli as a necessary precursor to concept formation is proposed and tested. In GIST (generalized invariance structure theory) invariants are detected via a perturbation mechanism of dimension suppression referred to as dimensional binding. Structural information acquired by this process is stored as a compound memory trace termed an ideotype. Ideotypes inform the subsystems that are responsible for learnability judgments, rule formation, and other types of concept representations. We show that GIST is more general (e.g., it works on continuous, semi-continuous, and binary stimuli) and makes much more accurate predictions than the leading models of concept learning difficulty,such as those based on a complexity reduction principle (e.g., number of mental models,structural invariance, algebraic complexity, and minimal description length) and those based on selective attention and similarity (GCM, ALCOVE, and SUSTAIN). GIST unifies these two key aspects of concept learning and categorization. Empirical evidence from three\ud experiments corroborates the predictions made by the theory and its core model which we propose as a candidate law of human conceptual behavior