Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Abstract

Dimension reduction is a preprocessing step in machine learning for eliminating undesirable features and increasing learning accuracy. In order to reduce the redundant features, there are data representation methods, each of which has its own advantages. On the other hand, big data with imbalanced classes is one of the most important issues in pattern recognition and machine learning. In this paper, a method is proposed in the form of a cost-sensitive optimization problem which implements the process of selecting and extracting the features simultaneously. The feature extraction phase is based on reducing error and maintaining geometric relationships between data by solving a manifold learning optimization problem. In the feature selection phase, the cost-sensitive optimization problem is adopted based on minimizing the upper limit of the generalization error. Finally, the optimization problem which is constituted from the above two problems is solved by adding a cost-sensitive term to create a balance between classes without manipulating the data. To evaluate the results of the feature reduction, the multi-class linear SVM classifier is used on the reduced data. The proposed method is compared with some other approaches on 21 datasets from the UCI learning repository, microarrays and high-dimensional datasets, as well as imbalanced datasets from the KEEL repository. The results indicate the significant efficiency of the proposed method compared to some similar approaches.

Models in the Construction of Accounting Informatization Transformation Based on Digital Twin

“…Ramos et al proposed the knowledge of seasonal climate prediction, and the environment of centralized data storage [9]. Fattahi et al considered dimensionality reduction as a preprocessing step in machine learning to remove unwanted features and improve learning accuracy [10]. However, the relevant researches only study the accuracy and efficiency of digital twins, and there are few studies that combine accounting information with digital twins.…”

“…e known Mahalanobis distance, Chebyshev distance, Minkowski distance, etc. can be used to calculate the expression for the distance between any two samples, Minkowski distance and Mahalanobis distance are as shown in formulas (10) and ( 11): e Minkowski distance is:…”