Are screening methods useful in feature selection? An empirical study

Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression model. While there are many such filter methods, there is a need for an objective evaluation of these methods. Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them. For this purpose, many popular screening methods are partnered in this paper with three regression learners and five classification learners and evaluated on ten real datasets to obtain accuracy criteria such as R-square and area under the ROC curve (AUC). The obtained results are compared through curve plots and comparison tables in order to find out whether screening methods help improve the performance of learning algorithms and how they fare with each other. Our findings revealed that the screening methods were useful in improving the prediction of the best learner on two regression and two classification datasets out of the ten datasets evaluated.Comment: 29 pages, 4 figures, 21 table

The role of beat gesture and pitch accent in semantic processing : An ERP study

Peer reviewedPublisher PD

BIOMECHANICAL ANALYSIS OF PULLING PHASES IN WEIGHT LIFTING – A CASE STUDY

The purpose of this study was to analyze specific aspects of weight lifting techniques. In analyses of snatch lift, identification of the pulling phase, in order to determine the critical points is the primary task. The kinematics and kinetics parameters are closely interrelated with pulling phases. Because of the differences in definitions of phase structures, the data from previous studies couldn’t be compared with each other directly. Therefore, this study investigated the phase structures in snatch lift and established the relationship between several variables of kinematics, such as the knee angle, the barbell vertical velocity and position, etc. It is clear that in the course of analyses, identifying the phases by means of the barbell vertical velocity, is the most convenient and logical method for obtaining fast feedback. Based on the above definitions of the phases, the mechanical data of two snatch lifts by an elite have been measured and analyze

Beat that Word : How Listeners Integrate Beat Gesture and Focus in Multimodal Speech Discourse

Peer reviewedPublisher PD

Semantic-aware blocking for entity resolution

In this paper, we propose a semantic-aware blocking framework for entity resolution (ER). The proposed framework is built using locality-sensitive hashing (LSH) techniques, which efficiently unifies both textual and semantic features into an ER blocking process. In order to understand how similarity metrics may affect the effectiveness of ER blocking, we study the robustness of similarity metrics and their properties in terms of LSH families. Then, we present how the semantic similarity of records can be captured, measured, and integrated with LSH techniques over multiple similarity spaces. In doing so, the proposed framework can support efficient similarity searches on records in both textual and semantic similarity spaces, yielding ER blocking with improved quality. We have evaluated the proposed framework over two real-world data sets, and compared it with the state-of-the-art blocking techniques. Our experimental study shows that the combination of semantic similarity and textual similarity can considerably improve the quality of blocking. Furthermore, due to the probabilistic nature of LSH, this semantic-aware blocking framework enables us to build fast and reliable blocking for performing entity resolution tasks in a large-scale data environment