Toward Optimal Feature Selection in Naive Bayes for Text Categorization

Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multi-class classification. Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination ($MD$) and $MD-\chi^2$ methods, for text categorization. The promising results of extensive experiments demonstrate the effectiveness of the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data Engineering. 14 pages, 5 figure

Executive Pay: Regulation vs. Market Competition

The economic slowdown and the active political season are generating calls for imposing new regulations on executive pay. The presidential candidates of the two major parties have lashed out at what they perceive to be excessive pay for certain executives or for corporate executives in general. Such populist sentiments are often based on misunderstandings about the role of corporate executives in the economy and the vigorous competition that exists for these highly skilled leaders. In the past, federal regulatory efforts based on such misunderstandings have generated unintended consequences, which have damaged the economy and hurt the ability of the market for executives to self-regulate over time. The labor market for executives and the associated pay levels are already subject to high levels of regulation. Indeed, U.S. corporations are subject to more stringent executive pay disclosure requirements than corporations anywhere else in the world. Before additional regulatory and legislative efforts are unleashed, policymakers should examine the rationale for current pay structures and the strong links between executive pay and corporate performance. The misperceptions that drive regulatory efforts are grounded in the idea that the market for executives is not competitive and that pay levels do not reflect supply and demand for talent. Critics claim that executives essentially set their own pay through their influence over the boards of directors of corporations. This "myth of managerial power" leads some policy makers to conclude that greater government regulation is necessary because the market is "rigged." However, a large body of empirical research documents that labor markets for executives are indeed competitive, and that pay levels track corporate performance. This study examines the market forces that set the parameters of executive compensation, the process that boards use to determine pay packages, and the data that indicate the efficient workings of the current "pay-for-performance" model. It also discusses the adverse consequences of imposing rules and regulations on an executive compensation system that has helped to generate great wealth for shareholders and millions of jobs for American workers

Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise

In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We show that the conventional approach with the assumption of Gaussian noise is a special case of the proposed method, and thus the new approach has more applicability. {The proposed detector is also tested on an independent component analysis (ICA) based unobservable false data attack scheme that utilizes similar assumptions of sample observation.} We evaluate the performance of the proposed state estimator and attack detector on the IEEE 30-bus power system with comparison to conventional Gaussian noise based detector. The superior performance of {both observable and unobservable false data attacks} demonstrates the effectiveness of the proposed approach and indicates a wide application on the power signal processing.Comment: 8 pages, 4 figures in IEEE Conference on Communications and Network Security (CNS) 201

Revisiting Local Campaign Effects: An Experiment Involving Literature Mail Drops in the 2007 Ontario Election

An invariant feature of constituency election campaigns is the literature mail drop, usually a one-page leaflet or card left at the door profiling the candidate and appealing for electoral support. In this article, we report on a field experiment designed to assess the effects of such mail drops. The experiment was conducted during the 2007 Ontario provincial election campaign in the constituency of Cambridge and entailed distributing literature for the Green party candidate in that constituency. After randomly assigning constituency polls to treatment and control groups, and delivering the Green candidate’s partisan literature only to the selected treatment group polls, we compared the candidate’s support levels in the treated polls with those in the control group. Our research detected a modest effect associated with the literature drop. The effect was largely limited to constituency neighbourhoods fitting at least part of the Green party’s traditional demographic, that is, those with higher than average socio-economic status

EEF: Exponentially Embedded Families with Class-Specific Features for Classification

In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from the PDF on features. With the PDF construction, we show that class-specific features can be used in the proposed classification method, instead of a common feature subset for all classes as used in conventional approaches. We apply the proposed EEF classifier for text categorization as a case study and derive an optimal Bayesian classification rule with class-specific feature selection based on the Information Gain (IG) score. The promising performance on real-life data sets demonstrates the effectiveness of the proposed approach and indicates its wide potential applications.Comment: 9 pages, 3 figures, to be published in IEEE Signal Processing Letter. IEEE Signal Processing Letter, 201

The 2007 Provincial Election and Electoral System Referendum in Ontario

Ontario’s general election in Oct. 10, 2007, was unprecedented for several reasons. The election was held on a date fixed by legislation and not one set by the premier or his caucus, something new to Ontario and relatively new to Canadian politics. Turnout declined to 53%, the lowest ever in Ontario history. The incumbent Liberals won a second consecutive majority government, something the party had not achieved since 1937. And finally, the election featured a referendum question that asked voters in Ontario to approve reforms to the electoral system, a proposal that was overwhelmingly rejected. This article explores each of the above-stated elements as they unfolded in the election

Exit Polling in Canada: An Experiment

Although exit polling has not been used to study Canadian elections before, such polls have methodological features that make them a potentially useful complement to data collected through more conventional designs. This paper reports on an experiment with exit polling in one constituency in the 2003 Ontario provincial election. Using student volunteers, a research team at Wilfrid Laurier University conducted an exit poll in the bellwether constituency of Kitchener Centre to assess the feasibility of mounting this kind of study on a broader scale. The experiment was successful in a number of respects. It produced a sample of 653 voters that broadly reflected the partisan character of the constituency, and which can hence be used to shed light on patterns of vote-switching and voter motivations in that constituency. It also yielded insights about best practices in mounting an exit poll in the Ontario context, as well as about the potential for using wireless communication devices to transmit respondent data from the field. The researchers conclude that exit polling on a limited basis (selected constituencies) is feasible, but the costs and logistics associated with this methodology make a province-wide or country-wide study unsupportable at present

Youth sport volunteering: developing social capital?

This paper analyses the capacity of youth sport volunteering to contribute to the development of social capital. Following a review of the emergence of social capital as a key theme in UK sport policy, the paper focuses on the ability of a structured sports volunteering programme to equip young people with skills for effective volunteering, and provide opportunities for 'social connectedness' through sports volunteer placements. The study uses survey data (n=160) and qualitative interviews (n=10) with young people to examine how the national Step into Sport programme impacts on participants' personal and skill development, and on their commitment to community involvement. Interviews with education and sport professionals (n=33) provide additional expert perspectives on the programme's impact on participants. Both sets of respondents report strong individual benefits to participants from their involvement, and increased social connectedness in a range of contexts. The paper concludes by considering the implications of the study for claims about the potential contribution of sport to social capital

A US hospital budget impact analysis of a skin closure system compared with standard of care in hip and knee arthroplasty.

Background: Medicare\u27s mandatory bundle for hip and knee arthroplasty necessitates provider accountability for quality and cost of care to 90 days, and wound closure may be a key area of consideration. The DERMABOND Methods: A 90-day economic model was developed assuming 500 annual hip/knee arthroplasties for a typical US hospital setting. In current practice, wound closure methods for the final skin layer were set to 50% sutures and 50% staples. In future practice, this distribution shifted to 20% sutures, 20% staples, and 60% Skin Closure System. Health care resources included materials (eg, staplers, steri-strips, and traditional/barbed sutures), standard or premium dressings, outpatient visits, and home care visits. An Expert Panel, comprised of three orthopedic physician assistants, two orthopedic surgeons, and a home health representative, was used to inform several model parameters. Other inputs were informed by national data or literature. Unit costs were based on list prices in 2016 US dollars. Uncertainty in the model was explored through one-way sensitivity and alternative scenario analyses. Results: The analysis predicted that use of Skin Closure System in the future practice could achieve cost savings of $56.70 to$79.62 per patient, when standard or premium wound dressings are used, respectively. This translated to an annual hospital budgetary savings ranging from $28,349 to$39,809 when assuming 500 arthroplasties. Dressing materials and postoperative health care visits were key model drivers. Conclusions: Use of the Skin Closure System may provide cost savings within hip and knee arthroplasties due to decreases in resource utilization in the postacute care setting