The Pertinent Single-Attribute-Based Classifier for Small Datasets Classification

Mona Mamdouh Jamjoom


Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In this paper, we revealed the power of a single attribute by introducing the pertinent single-attribute-based-heterogeneity-ratio classifier (SAB-HR) that uses a pertinent attribute to classify small datasets. The SAB-HR’s uses feature selection method, which uses the Heterogeneity-Ratio (H-Ratio) measure to identify the most homogeneous attribute among the other attributes in the set. Our empirical results on 12 benchmark datasets from a UCI machine learning repository showed that the SAB-HR classifier significantly outperformed the classical OneR classifier for small datasets. In addition, using the H-Ratio as a feature selection criterion for selecting the single attribute was more effectual than other traditional criteria, such as Information Gain (IG) and Gain Ratio (GR).


single-attribute-based classifier; OneR classifier; small dataset; feature selection; Heterogeneity Ratio; classification


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