Cumulative dominance and heuristic performance in binary multi-attribute choice

Several studies have reported high performance of simple decision heuristics multi-attribute decision making. In this paper, we focus on situations where attributes are binary and analyze the performance of Deterministic-Elimination-By-Aspects (DEBA) and similar decision heuristics. We consider non-increasing weights and two probabilistic models for the attribute values: one where attribute values are independent Bernoulli randomvariables; the other one where they are binary random variables with inter-attribute positive correlations. Using these models, we show that good performance of DEBA is explained by the presence of cumulative as opposed to simple dominance. We therefore introduce the concepts of cumulative dominance compliance and fully cumulative dominance compliance and show that DEBA satisfies those properties. We derive a lower bound with which cumulative dominance compliant heuristics will choose a best alternative and show that, even with many attributes, this is not small. We also derive an upper bound for the expected loss of fully cumulative compliance heuristics and show that this is moderateeven when the number of attributes is large. Both bounds are independent of the values ofthe weights.

Implementing TURF analysis through binary linear programming

This paper introduces the approach of using Total Unduplicated Reach and Frequency analysis (TURF) to design a product line through a binary linear programming model. This improves the efficiency of the search for the solution to the problem compared to the algorithms that have been used to date. The results obtained through our exact algorithm are presented, and this method shows to be extremely efficient both in obtaining optimal solutions and in computing time for very large instances of the problem at hand. Furthermore, the proposed technique enables the model to be improved in order to overcome the main drawbacks presented by TURF analysis in practice.

Ignoring information in binary choice with continuous variables: When is less 'more'?

When can a single variable be more accurate in binary choice than multiple sources of information? We derive analytically the probability that a single variable (SV) will correctly predict one of two choices when both criterion and predictor are continuous variables. We further provide analogous derivations for multiple regression (MR) and equal weighting (EW) and specify the conditions under which the models differ in expected predictive ability. Key factors include variability in cue validities, intercorrelation between predictors, and the ratio of predictors to observations in MR. Theory and simulations are used to illustrate the differential effects of these factors. Results directly address why and when one-reason decision making can be more effective than analyses that use more information. We thus provide analytical backing to intriguing empirical results that, to date, have lacked theoretical justification. There are predictable conditions for which one should expect less to be more.

Reliability of the image evaluation system PGMI

Purpose: Many countries used the PGMI (P=perfect, G=good, M=moderate, I=inadequate) classification system for assessing the quality of mammograms. Limits inherent to the subjectivity of this classification have been shown. Prior to introducing this system in Switzerland, we wanted to better understand the origin of this subjectivity in order to minimize it. Our study aimed at identifying the main determinants of the variability of the PGMI system and which criteria are the most subjected to subjectivity. Methods and Materials: A focus group composed of 2 experienced radiographers and 2 radiologists specified each PGMI criterion. Ten raters (6 radiographers and 4 radiologists) evaluated twice a panel of 40 randomly selected mammograms (20 analogic and 20 digital) according to these specified PGMI criteria. The PGMI classification was assessed and the intra- and inter-rater reliability was tested for each professional group (radiographer vs radiologist), image technology (analogic vs digital) and PGMI criterion. Results: Some 3,200 images were assessed. The intra-rater reliability appears to be weak, particularly in respect to inter-rater variability. Subjectivity appears to be largely independent of the professional group and image technology. Aspects of the PGMI classification criteria most subjected to variability were identified. Conclusion: Post-test discussions enabled to specify more precisely some criteria. This should reduce subjectivity when applying the PGMI classification system. A concomitant, important effort in training radiographers is also necessary.

Comparison of organ doses and image quality between CT and flat panel XperCT scans in wrist and inner ear examinations.

The aim of this study was to evaluate and compare organ doses delivered to patients in wrist and petrous bone examinations using a multislice spiral computed tomography (CT) and a C-arm cone-beam CT equipped with a flat-panel detector (XperCT). For this purpose, doses to the target organ, i.e. wrist or petrous bone, together with those to the most radiosensitive nearby organs, i.e. thyroid and eye lens, were measured and compared. Furthermore, image quality was compared for both imaging systems and different acquisition modes using a Catphan phantom. Results show that both systems guarantee adequate accuracy for diagnostic purposes for wrist and petrous bone examinations. Compared with the CT scanner, the XperCT system slightly reduces the dose to target organs and shortens the overall duration of the wrist examination. In addition, using the XperCT enables a reduction of the dose to the eye lens during head scans (skull base and ear examinations).

Habitudes alimentaires et image corporelle chez des étudiants de deuxième année de gymnase à Lausanne [Food habits and body image of students in Lausanne]

During adolescence, nutrition needs are high; however the literature shows that few adolescents are following standardized nutritional requirements. A few weeks before an intervention about nutrition to high school adolescents in Lausanne, they were invited to fill in a self-reported questionnaire about their nutrition modes and habits, and their self-image satisfaction (N = 198). Results show that only 5% of youth are eating 5 fruits and vegetables per day and only 29% 3 to 5 dairy products. 21% of female and 6% of boys are not satisfied about their self-image, and those exhibiting a poor self-image tend to adopt health compromising eating patterns in a higher proportion. During adolescence it is important not only to investigate the nutritional habits but also one's self image.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.