BIDI: A classification algorithm with instance difficulty invariance

Abstract In artificial intelligence, an expert/intelligent systems can emulate the decision-making ability of human experts. A good classification algorithm can provide significant assistance to expert/intelligent systems in solving a variety of practical problems. In classification, the “hard” instances may be outliers or noisy instances that are difficult to learn, which may confuse the classifier and induce the overfitting problem in the case of placing much emphasis on them. In fact, the difficulty of instances is crucial for improving the generalization and credibility of classification. Unfortunately, nearly all the existing classifiers ignore this important information. In this paper, the classification difficulty of each instance is introduced from a statistical perspective, which is an inherent characteristic of the instance itself. Then, a new classification algorithm named “boosting with instance difficulty invariance (BIDI)” is proposed by incorporating the classification difficulty of instances. The BIDI conforms to the human cognition that easy instances are misclassified with a lower probability than difficult ones, and performs better with respect to generalization. The key insight of BIDI can provide relevant guidance for researchers to improve the generalization and credibility of classifiers in the expert systems of decision support systems. Experimental results demonstrate the effectiveness of BIDI in real-world data sets, indicating that it has great potential for solving many classification tasks of expert systems such as disease diagnosis and credit card fraud detection. Although the classification difficulty has strong statistical significance, its implementation remains computationally expensive. A fast method demonstrating rationality and feasibility is also proposed to approximate instances’ classification difficulty.