A low variance error boosting algorithm

This paper introduces a robust variant of AdaBoost, cw-AdaBoost, that uses weight perturbation to reduce variance error, and is particularly effective when dealing with data sets, such as microarray data, which have large numbers of features and small number of instances. The algorithm is compared with AdaBoost, Arcing and MultiBoost, using twelve gene expression datasets, using 10-fold cross validation. The new algorithm consistently achieves higher classification accuracy over all these datasets. In contrast to other AdaBoost variants, the algorithm is not susceptible to problems when a zero-error base classifier is encountered

Payment card rewards programs and consumer payment choice

Card payments have been growing very rapidly. To continue the growth, payment card networks keep adding new merchants and card issuers try to stimulate their existing customers’ card usage by providing rewards. This paper seeks to analyze the effects of payment card rewards programs on consumer payment choice, by using consumer survey data. Specifically, we examine whether credit/debit reward receivers use credit/debit cards relatively more often than other consumers, if so how much more often, and which payment methods are replaced by reward card payments. Our results suggest that (i) consumers with credit card rewards use credit cards much more exclusively than those without credit card rewards; (ii) even among those who carry a credit card balance, consumers with credit card rewards use a credit card more often than those without rewards; (iii) among consumers who receive credit card rewards, those who receive credit card rewards as well as debit card rewards tend to use debit cards more often than those who receive credit card rewards only; and (iv) reward card transactions seem to replace not only paper-based transactions but also non-reward card transactions.

Payment Card Rewards Programs and Consumer Payment Choice

We estimate the direct effects of rewards card programs on consumer payment choice for in-store transactions. By using a data set that contains information on consumer perceived attributes of payment methods and consumer perceived acceptance of payment methods by merchants, we control for consumer heterogeneity in preferences and choice sets. We conduct policy experiments to examine the effects of removing rewards from credit and/or debit cards. The results suggest that: (i) only a small percentage of consumers would switch from electronic to paper-based payment methods, (ii) the effect of removing credit card rewards is greater than that of removing debit card rewards, and consequently, (iii) removing rewards on both credit and debit cards would reduce credit card transactions, but increase debit card transactions.Consumer Choice; Payment Methods; Rewards Programs; Interchange fees

Artificial intelligent vision analysis in obstructive sleep apnoea (OSA)

Although polysomnography is a generally adopted approach for diagnosing obstructive sleep apnoea (OSA), there are several critical drawbacks with it, including massive equipment cost, large expense on replacing damaged components and more importantly invasive devices required to be worn while patients are struggling to sleep. Furthermore, there is no proof that polymonography obtains higher accuracy in detecting patients with OSA than more simple investigations [1]. Video monitoring has been adopted to assist diagnosis on obstructive sleep apnoea. From practical researches [3], the best predictors of morbidity in individual patients, as assessed by improvements with CPAP therapy, are nocturnal oxygen saturation [4, 5] and movement during sleep [4]. Hence, we purpose a robotic, objective and reliable video monitoring system with AI intelligence for analysis on human behavior during sleep, automatically generating a statistics report on body activity, including arm movement, limb movement, head movement and body rotation movement and arousal movement

Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea

Recognizing abnormal breathing activity from body movement is a challenging task in machine vision. In this paper, we present a non-intrusive automatic video monitoring technique for detecting abnormal breathing activities and assisting in diagnosis of obstructive sleep apnoea. The proposed technique utilizes infrared video information and avoids imposing geometric or positional constraints on the patient. The technique also deals with fully or partially obscured patients’ body. A continuously updated breathing activity template is built for distinguishing general body movement from breathing behavior

The Price Consideration Model of Brand Choice

The workhorse brand choice models in marketing are the multinomial logit (MNL) and nested multinomial logit (NMNL). These models place strong restrictions on how brand share and purchase incidence price elasticities are related. In this paper, we propose a new model of brand choice, the “price consideration” (PC) model, that allows more flexibility in this relationship. In the PC model, consumers do not observe prices in each period. Every week, a consumer decides whether to consider a category. Only then does he/she look at prices and decide whether and what to buy. Using scanner data, we show the PC model fits much better than MNL or NMNL. Simulations reveal the reason: the PC model provides a vastly superior fit to inter-purchase spells.Brand Choice; Purchase Incidence; Price Elasticity; Inter-purchase Spell

A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models

This paper provides a step-by-step guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed by Imai Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm, which solves the DDP model and estimates its structural parameters simultaneously. The main computational advantage of this estimation algorithm is the efficient use of information obtained from the past iterations. In the conventional Nested Fixed Point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the Bayesian Dynamic Programming algorithm extensively uses the computational results obtained from the past iterations to help solving the DDP model at the current iterated parameter values. Consequently, it significantly alleviates the computational burden of estimating a DDP model. We carefully discuss how to implement the algorithm in practice, and use a simple dynamic store choice model to illustrate how to apply this algorithm to obtain parameter estimates.Bayesian Dynamic Programming, Discrete Choice Dynamic Programming, Markov Chain Monte Carlo

Memories of the future

The year is 2020. Sheffield University’s MSc in Electronic & Digital Library Management has been running for 10 years. What paths have its graduates’ careers taken