Data treatment effects on classification accuracies of bipedal running and walking motions

Abstract

Many real-world data can be irrelevant, redundant, inconsistent, noisy or incomplete. To extract qualitative data for classification analysis, efficient data preprocessing techniques such as data transformation, data compression, feature extraction and imputation are required. This study investigates three data treatment approaches: randomization; attribute elimination and missing values imputation on bipedal motion data. The effects of data treatment were examined on classification accuracies to retrieve informative attributes. The analysis is performed on bipedal running and walking motions concerning the human and ostrich obtained from public available domain and a real case study. The classification accuracies were tested on seven classifier categories aided by the WEKA tool. The findings show enhancements in classification accuracies for treated dataset in bipedal run and walk with respective enhancements of 3.21% and 2.29% in treated data compared to the original. The findings support the integration of data randomization and selective attribute elimination treatment for better effects in classification analysis.