Popular Ensemble Methods: An Empirical Study

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees

Black Male Collegiate Athletes’ Perceptions of Their Career and Academic Preparation: A Mixed Methods Study

We employed a mixed methods approach with sequential explanatory design (Creswell & Plano Clark, 2017) and a Social Capital Theory framework (Bourdieu, 1977) to investigate three research questions: (1) In what ways were participants’ career and college readiness capital developed during high school? (2) How do participants view their academic and career growth and development prior to and after coming to college? (3) Who provided career and college development to participants in this study prior to their college entrance? Results revealed potential reasons why disparities existed between Black and White participants beginning in K-12 and continuing through college. Implications for anti-racist school counseling are given

Do Characteristics of Faces That Convey Trustworthiness and Dominance Underlie Perceptions of Criminality?

BACKGROUND: This study tested whether the 2D face evaluation model proposed by Oosterhof and Todorov can parsimoniously account for why some faces are perceived as more criminal-looking than others. The 2D model proposes that trust and dominance are spontaneously evaluated from features of faces. These evaluations have adaptive significance from an evolutionary standpoint because they indicate whether someone should be approached or avoided. METHOD: Participants rated the emotional state, personality traits, and criminal appearance of faces shown in photographs. The photographs were of males and females taken under naturalistic conditions (i.e., police mugshots) and highly controlled conditions. In the controlled photographs, the emotion display of the actor was systematically varied (happy expression, emotionally neutral expression, or angry expression). RESULTS: Both male and female faces rated high in criminal appearance were perceived as less trustworthy and more dominant in police mugshots as well as in photographs taken under highly controlled conditions. Additionally, emotionally neutral faces were deemed as less trustworthy if they were perceived as angry, and more dominant if they were morphologically mature. Systematically varying emotion displays also affected criminality ratings, with angry faces perceived as the most criminal, followed by neutral faces and then happy faces. CONCLUSION: The 2D model parsimoniously accounts for criminality perceptions. This study extends past research by demonstrating that morphological features that signal high dominance and low trustworthiness can also signal high criminality. Spontaneous evaluations regarding criminal propensity may have adaptive value in that they may help us to avoid someone who is physically threatening. On the other hand, such evaluations could inappropriately influence decision making in criminal identification lineups. Hence, additional research is needed to discover whether and how people can avoid making evaluations regarding criminality from a person's facial appearance

Spiritual Formation among Doctoral Psychology Students in Explicitly Christian Programs

How does training in an explicitly Christian doctoral program in clinical psychology affect students\u27 faith development? Two studies are reported that consider students\u27 locus of control, spiritual perceptions, and religious behaviors over the course of training. The first study involved 157 students from 5 doctoral programs who completed questionnaires at the beginning and end of an academic year. A number of changes were reported from the beginning to the end of the year, including increased internal locus of control, decreased awareness of God. decreased church attendance, and decreased ratings on the importance of religion. A number of differences between cohorts were also observed, with flrst-year students affirming more spiritual attributions, religious problem-solving, and religious behaviors than students in other cohorts. The second study included 140 first- and second-year students from 4 doctoral programs. Changes were reported over the academic year, including increased disappointment with God and fatigue, and decreased church attendance, personal prayer, and importance of religion. No differences between first and second-year students were observed. Various possible explanations are offered for these findings, including eroding of faith, enhanced self-efficacy, rearraging faith, and fatigue

Using informative behavior to increase engagement while learning from human reward

In this work, we address a relatively unexplored aspect of designing agents that learn from human reward. We investigate how an agent’s non-task behavior can affect a human trainer’s training and agent learning. We use the TAMER framework, which facilitates the training of agents by human-generated reward signals, i.e., judgements of the quality of the agent’s actions, as the foundation for our investigation. Then, starting from the premise that the interaction between the agent and the trainer should be bi-directional, we propose two new training interfaces to increase a human trainer’s active involvement in the training process and thereby improve the agent’s task performance. One provides information on the agent’s uncertainty which is a metric calculated as data coverage, the other on its performance. Our results from a 51-subject user study show that these interfaces can induce the trainers to train longer and give more feedback. The agent’s performance, however, increases only in response to the addition of performance-oriented information, not by sharing uncertainty levels. These results suggest that the organizational maxim about human behavior, “you get what you measure”—i.e., sharing metrics with people causes them to focus on optimizing those metrics while de-emphasizing other objectives—also applies to the training of agents. Using principle component analysis, we show how trainers in the two conditions train agents differently. In addition, by simulating the influence of the agent’s uncertainty–informative behavior on a human’s training behavior, we show that trainers could be distracted by the agent sharing its uncertainty levels about its actions, giving poor feedback for the sake of reducing the agent’s uncertainty without improving the agent’s performance

Ensemble of a subset of kNN classifiers

Combining multiple classifiers, known as ensemble methods, can give substantial improvement in prediction performance of learning algorithms especially in the presence of non-informative features in the data sets. We propose an ensemble of subset of kNN classifiers, ESkNN, for classification task in two steps. Firstly, we choose classifiers based upon their individual performance using the out-of-sample accuracy. The selected classifiers are then combined sequentially starting from the best model and assessed for collective performance on a validation data set. We use bench mark data sets with their original and some added non-informative features for the evaluation of our method. The results are compared with usual kNN, bagged kNN, random kNN, multiple feature subset method, random forest and support vector machines. Our experimental comparisons on benchmark classification problems and simulated data sets reveal that the proposed ensemble gives better classification performance than the usual kNN and its ensembles, and performs comparable to random forest and support vector machines

Prototype effect and the persuasiveness of generalizations

An argument that makes use of a generalization activates the prototype for the category used in the generalization. We conducted two experiments that investigated how the activation of the prototype affects the persuasiveness of the argument. The results of the experiments suggest that the features of the prototype overshadow and partly overwrite the actual facts of the case. The case is, to some extent, judged as if it had the features of the prototype instead of the features it actually has. This prototype effect increases the persuasiveness of the argument in situations where the audience finds the judgment more warranted for the prototype than for the actual case (positive prototype effect), but decreases persuasiveness in situations where the audience finds the judgment less warranted for the prototype than for the actual case (negative prototype effect)

Adaptation-Based Programming in Haskell

We present an embedded DSL to support adaptation-based programming (ABP) in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. We show how our design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. We also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs. Over repeated executions, these adaptive values adjust to more efficient ones and enable the user's programs to self optimize. The design of our DSL depends significantly on the use of type classes. We will illustrate, along with presenting our DSL, how the use of type classes can support the gradual evolution of DSLs.Comment: In Proceedings DSL 2011, arXiv:1109.032

On the Perception of Religious Group Membership from Faces

BACKGROUND: The study of social categorization has largely been confined to examining groups distinguished by perceptually obvious cues. Yet many ecologically important group distinctions are less clear, permitting insights into the general processes involved in person perception. Although religious group membership is thought to be perceptually ambiguous, folk beliefs suggest that Mormons and non-Mormons can be categorized from their appearance. We tested whether Mormons could be distinguished from non-Mormons and investigated the basis for this effect to gain insight to how subtle perceptual cues can support complex social categorizations. METHODOLOGY/PRINCIPAL FINDINGS: Participants categorized Mormons' and non-Mormons' faces or facial features according to their group membership. Individuals could distinguish between the two groups significantly better than chance guessing from their full faces and faces without hair, with eyes and mouth covered, without outer face shape, and inverted 180°; but not from isolated features (i.e., eyes, nose, or mouth). Perceivers' estimations of their accuracy did not match their actual accuracy. Exploration of the remaining features showed that Mormons and non-Mormons significantly differed in perceived health and that these perceptions were related to perceptions of skin quality, as demonstrated in a structural equation model representing the contributions of skin color and skin texture. Other judgments related to health (facial attractiveness, facial symmetry, and structural aspects related to body weight) did not differ between the two groups. Perceptions of health were also responsible for differences in perceived spirituality, explaining folk hypotheses that Mormons are distinct because they appear more spiritual than non-Mormons. CONCLUSIONS/SIGNIFICANCE: Subtle markers of group membership can influence how others are perceived and categorized. Perceptions of health from non-obvious and minimal cues distinguished individuals according to their religious group membership. These data illustrate how the non-conscious detection of very subtle differences in others' appearances supports cognitively complex judgments such as social categorization