# An Efficient AdaBoost Algorithm with the Multiple Thresholds Classification
## 1 Introduction  
The rapid growth of the Internet has led to a dramatic increase in the rate of data generation. Data mining technology is one of the most important means of mining value from such a large amount of data. Classification is the initialization operation that processes the digitized information of data mining. Obviously, accurate classification will save a lot of time and economic costs for subsequent work, such as analysis, forecasting, and fitting processes. Ensemble methods are ideal for regression and classification, and by combining multiple models into a very reliable model, it can reduce bias and variance to boost the accuracy of predictions [1, 2]. Two common techniques for constructing Ensemble classifiers are Boosting [3, 4, 5, 6] and Bagging [8, 9, 10]. Boosting is better than Bagging, with less noise in the data [11]. In the field of machine learning, the boosting algorithm is a more classic general-purpose learning algorithm, which is based on the \"probably approximately correct\" learning model proposed by [PERSON]. [PERSON] and [PERSON] improved the Boosting algorithm in 1995 and named it the AdaBoost algorithm. The AdaBoost algorithm has now developed into an important feature classification algorithm in machine learning, which has been widely used in face detection [12] and image retrieval [13], intrusion detection [14], object recognition [15], feature extraction [16], and other applications. In the training phase, the weights assigned to the samples by AdaBoost rise as the error rate increases, and conversely, the newly assigned weights fall as the error rate decreases. The samples are then continuously trained with unknown distribution weights. The aim is to obtain powerful feedback by reducing errors on the next machine and ultimately achieving better accuracy [17]. How to design an effective classifier [18] to improve the accuracy of classification is at the core of this type of algorithm. In previous years, the AdaBoost algorithm has attracted great interest of scientists, aiming at the problems of insufficient correct classification rate and long training time, a variety of improved algorithms were proposed [19; 20]. One of the scholars focused on solving the problem of unbalanced data classification [21; 22] to improve the classification accuracy of the algorithm. [PERSON] and [PERSON] proposed a new reconfigurable Weak Learn algorithm based on the decision tree algorithm [23]. [PERSON] and [PERSON] analyzed a robust classification algorithm called Robust Imprecise Local Boost (RILBoost), which focused on the precise vector of weights assigned for examples in the training set at each iteration of boosting improved classification performance [24]. Another class of scholars has designed a variety of important and influential AdaBoost variants under the framework of forward Additive Stagewise models based on nonparametric regression, including LogitBoost [25], GradientBoost [26], Gradient-boost-tree [26], and ProbitBoost [27] for solving probabilistic regression. In these experiments, they demonstrated that the AdaBoost-based approach provided better detection performance than other non-AdaBoost-based methods. However, algorithms are difficult to coexist with high classification accuracy and low complexity, that is, strong classifiers are accurate but difficult to obtain, while weak classifiers are the opposite. The Boosting algorithm requires the Weak Learn algorithm to have an accuracy better than 0.5, which is too strong to be fulfilled, and the number of training examples can have a significant impact on accuracy. Studies have shown that even under ideal circumstances, increasing the number of iterations, AdaBoost will eventually overfit [28], and the sub-classifiers generated at the end of the iteration will have very small effect on improving the generalization performance of the classifier, and there is not only a risk of overfitting, but also a waste of computing power.