A Novel Hybrid Ordinal Learning Model with Health Care Application

Ordinal learning (OL) is a type of machine learning models with broad utility in health care applications such as diagnosis of different grades of a disease (e.g., mild, modest, severe) and prediction of the speed of disease progression (e.g., very fast, fast, moderate, slow). This paper aims to tackle a situation when precisely labeled samples are limited in the training set due to cost or availability constraints, whereas there could be an abundance of samples with imprecise labels. We focus on imprecise labels that are intervals, i.e., one can know that a sample belongs to an interval of labels but cannot know which unique label it has. This situation is quite common in health care datasets due to limitations of the diagnostic instrument, sparse clinical visits, or/and patient dropout. Limited research has been done to develop OL models with imprecise/interval labels. We propose a new Hybrid Ordinal Learner (HOL) to integrate samples with both precise and interval labels to train a robust OL model. We also develop a tractable and efficient optimization algorithm to solve the HOL formulation. We compare HOL with several recently developed OL methods on four benchmarking datasets, which demonstrate the superior performance of HOL. Finally, we apply HOL to a real-world dataset for predicting the speed of progressing to Alzheimer's Disease (AD) for individuals with Mild Cognitive Impairment (MCI) based on a combination of multi-modality neuroimaging and demographic/clinical datasets. HOL achieves high accuracy in the prediction and outperforms existing methods. The capability of accurately predicting the speed of progression to AD for each individual with MCI has the potential for helping facilitate more individually-optimized interventional strategies.Comment: 16 pages, 3 figures, 2 table

Comparative Study on the Performance of Categorical Variable Encoders in Classification and Regression Tasks

Categorical variables often appear in datasets for classification and regression tasks, and they need to be encoded into numerical values before training. Since many encoders have been developed and can significantly impact performance, choosing the appropriate encoder for a task becomes a time-consuming yet important practical issue. This study broadly classifies machine learning models into three categories: 1) ATI models that implicitly perform affine transformations on inputs, such as multi-layer perceptron neural network; 2) Tree-based models that are based on decision trees, such as random forest; and 3) the rest, such as kNN. Theoretically, we prove that the one-hot encoder is the best choice for ATI models in the sense that it can mimic any other encoders by learning suitable weights from the data. We also explain why the target encoder and its variants are the most suitable encoders for tree-based models. This study conducted comprehensive computational experiments to evaluate 14 encoders, including one-hot and target encoders, along with eight common machine-learning models on 28 datasets. The computational results agree with our theoretical analysis. The findings in this study shed light on how to select the suitable encoder for data scientists in fields such as fraud detection, disease diagnosis, etc

Union Mediation and Adaptation to Reciprocal Loyalty Arrangements

This study assesses the industrial relations application of the “loyalty-exit-voice” proposition. The loyalty concept is linked to reciprocal employer-employee arrangements and examined as a job attribute in a vignette questionnaire distributed to low and medium-skilled employees. The responses provided by employees in three European countries indicate that reciprocal loyalty arrangements, which involve the exchange of higher effort for job security, are one of the most desirable job attributes. This attribute exerts a higher impact on the job evaluations provided by unionised workers, compared to their non-union counterparts. This pattern is robust to a number of methodological considerations. It appears to be an outcome of adaptation to union mediated cooperation. Overall the evidence suggests that the loyalty-job evaluation profiles of unionised workers are receptive to repeated interaction and negative shocks, such as unemployment experience. This is not the case for the non-union workers. Finally, unionised workers appear to “voice” a lower job satisfaction, but exhibit low “exit” intentions, compared to the non-unionised labour.EPICURUS, a project supported by the European Commission through the 5th Framework Programme “Improving Human Potential” (contract number: HPSE-CT-2002-00143

Unionism and peer-referencing

This study assesses the “fair-wage-effort” hypothesis, by examining (a) the relationship between relative wage comparisons and job satisfaction and quitting intensions, and (b) the relative ranking of stated effort inducing-incentives, in a novel dataset of unionised and non-unionised European employees. By distinguishing between downward and upward-looking wage comparisons, it is shown that wage comparisons to similar workers exert an asymmetric impact on the job satisfaction of union workers, a pattern consistent with inequity-aversion and conformism to the reference point. Moreover, union workers evaluate peer observation and good industrial relations more highly than payment and other incentives. In contrast, non-union workers are found to be more status-seeking in their satisfaction responses and less dependent on their peers in their effort choices The results are robust to endogenous union membership, considerations of generic loss aversion and across different tenure profiles. They are supportive of the individual egalitarian bias of collective wage determination and self-enforcing effort norms.EPICURUS, a project supported by the European Commission through the 5th Framework Programme “Improving Human Potential” (contract number: HPSE-CT-2002-00143

Sex differences in emotional evaluation of film clips: interaction with five high arousal emotional categories

The present study aimed to investigate gender differences in the emotional evaluation of 18 film clips divided into six categories: Erotic, Scenery, Neutral, Sadness, Compassion, and Fear. 41 female and 40 male students rated all clips for valence-pleasantness, arousal, level of elicited distress, anxiety, jittery feelings, excitation, and embarrassment. Analysis of positive films revealed higher levels of arousal, pleasantness, and excitation to the Scenery clips in both genders, but lower pleasantness and greater embarrassment in women compared to men to Erotic clips. Concerning unpleasant stimuli, unlike men, women reported more unpleasantness to the Compassion, Sadness, and Fear compared to the Neutral clips and rated them also as more arousing than did men. They further differentiated the films by perceiving greater arousal to Fear than to Compassion clips. Women rated the Sadness and Fear clips with greater Distress and Jittery feelings than men did. Correlation analysis between arousal and the other emotional scales revealed that, although men looked less aroused than women to all unpleasant clips, they also showed a larger variance in their emotional responses as indicated by the high number of correlations and their relatively greater extent, an outcome pointing to a masked larger sensitivity of part of male sample to emotional clips. We propose a new perspective in which gender difference in emotional responses can be better evidenced by means of film clips selected and clustered in more homogeneous categories, controlled for arousal levels, as well as evaluated through a number of emotion focused adjectives