As the core of artificial intelligence, machine learning plays a very important role in the realization of computer intelligence. With the continuous innovation of science and technology and the rapid development of computer network technology, machine learning has become an increasingly important part of the field of artificial intelligence, and the classification problem in machine learning is also one of the main tasks of machine learning, which is widely used in various fields of real life, and the accuracy rate of the goals it can achieve is getting higher and higher. Among them, the binary classification problem is also an important part of machine learning; whether it is medical, agriculture or daily production and life, the problem of binary classification is everywhere. Looking for a more accurate algorithm to solve the binary classification problem is an important research direction in the field of artificial intelligence.
The advent of the information revolution and the era of big data promoted the development of artificial intelligence, followed by the need to find how to save, extract, and process the required factor data for huge data. Causal analysis between factors provides an important tool for artificial intelligence, data mining, etc. However, the main difficulty in the field of artificial intelligence is that the key factors to solve practical problems have not been revealed, and how to find the key factors has become an important research direction. Factor explicitness is a new theory under the factor space theory proposed by Wang Peizhuang [
1]. As a bottleneck problem in the field of artificial intelligence, factor explicitness has great significance in helping artificial intelligence problems find the key factors. As long as the key factors are found, the corresponding problems will naturally be solved. Sun Hui et al. [
2] proposed a serial sweep algorithm. Aiming at the classification problem of machine learning, the algorithm defines the sweeping direction and the explicit and implicit factors by using the factor space theory. In order to reduce algorithm’s complexity, the ordered set of swept class vectors is defined, and the factor implicit model is constructed. The results of numerical experiments show that the algorithm is feasible and effective. Zeng Fanhui et al. [
3] proposed the application of the serial scanning algorithm in multi-classification. On the basis of this algorithm, this paper proposes a fine-tuning sweeping learning algorithm, a dimension-raising side-by-side serial scanning algorithm, and a combination algorithm [
4] to solve the two problems in the operation of the serial sweep classification algorithm.