Affiliations: USICT, Guru Gobind Singh Indraprasth University, Sector-16, C, Dwarka, New Delhi, India
Correspondence:
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Corresponding author. Deepika Singh, USICT, Guru Gobind Singh Indraprasth University, Sector-16 C, Dwarka, New Delhi, India. E-mail: deep.16feb84@gmail.com.
Abstract: Imbalanced dataset classification is challenging because of the severely skewed class distribution. The traditional machine learning algorithms show degraded performance for these skewed datasets. However, there are additional characteristics of a classification dataset that are not only challenging for the traditional machine learning algorithms but also increase the difficulty when constructing a model for imbalanced datasets. Data complexity metrics identify these intrinsic characteristics, which cause substantial deterioration of the learning algorithms’ performance. Though many research efforts have been made to deal with class noise, none of them focused on imbalanced datasets coupled with other intrinsic factors. This paper presents a novel hybrid pre-processing algorithm focusing on treating the class-label noise in the imbalanced dataset, which suffers from other intrinsic factors such as class overlapping, non-linear class boundaries, small disjuncts, and borderline examples. This algorithm uses the wCM complexity metric (proposed for imbalanced dataset) to identify noisy, borderline, and other difficult instances of the dataset and then intelligently handles these instances. Experiments on synthetic datasets and real-world datasets with different levels of imbalance, noise, small disjuncts, class overlapping, and borderline examples are conducted to check the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm offers an interesting alternative to popular state-of-the-art pre-processing algorithms for effectively handling imbalanced datasets along with noise and other difficulties.
Keywords: Classification, class imbalance, data complexity, overlapping, bayes error, pre-processing, learning algorithms
