Gait-based Gender Classification Considering Resampling and Feature Selection

Two intrinsic data characteristics that arise in many domains are the class imbalance and the high dimensionality, which pose new challenges that should be addressed. When using gait for gender classification, benchmarking public databases and renowned gait representations lead to these two problems, but they have not been jointly studied in depth. This paper is a preliminary study that pursues to investigate the benefits of using several techniques to tackle the aforementioned problems either singly or in combination, and also to evaluate the order of application that leads to the best classification performance. Experimental results show the importance of jointly managing both problems for gait-based gender classification. In particular, it seems that the best strategy consists of applying resampling followed by feature selection

Classification model and analysis on students’ performance

The purpose of this paper is to propose a classification model for classifying students’performance in SijilPelajaran Malaysia in order to help teachers plan suitable teachingactivities for their students based on the students’ performance. Five classifier algorithms have been used during the process which are Naïve Bayes, Random Tree, Multi Class Classifier, Conjunctive Rule and Nearest Neighbour. Data was collected from MaktabRendahSains MARA Kuala Berang, Terengganu, Malaysia starting May 2011 until December 2014. The students’ performance was evaluated based on the category of students according to their SPM Results. Parameters that contribute to students’ performance such as stream, state, gender and hometown are also investigated along with the examination data.This research shows that first semester results can be used to identify students’ performance.Keywords: educational data mining; classification model; feature selection