Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection

Abstract

The Adaptive Boosting (AdaBoost) classifier is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost classifier directly to pulmonary nodule detection of labeled and unlabeled lung CT images since there are still some drawbacks to ensemble learning method. Therefore, to solve the labeled and unlabeled data classification problem, the semi-supervised AdaBoost classifier using an improved sparrow search algorithm (AdaBoost-ISSA-S4VM) was established. Firstly, AdaBoost classifier is used to construct a strong semi-supervised classifier using several weak classifiers S4VM (AdaBoost-S4VM). Next, in order to solve the accuracy problem of AdaBoost-S4VM, sparrow search algorithm (SSA) is introduced in the AdaBoost classifier and S4VM. Then, sine cosine algorithm and new labor cooperation structure are adopted to increase the global optimal solution and convergence performance of sparrow search algorithm, respectively. Furthermore, based on the improved sparrow search algorithm and adaptive boosting classifier, the AdaBoost-S4VM classifier is improved. Finally, the effective improved AdaBoost-ISSA-S4VM classification model was developed for actual pulmonary nodule detection based on the publicly available LIDC-IDRI database. The experimental results have proved that the established AdaBoost-ISSA-S4VM classification model has good performance on labeled and unlabeled lung CT images.

Bearing Fault Diagnosis Method Based on Multidomain Heterogeneous Information Entropy Fusion and Model Self-Optimisation

“…e SSA algorithm is a new swarm intelligence optimisation algorithm that was proposed by Xue and Shen [10] in 2020. e algorithm has the characteristics of few parameters that require adjustment and good stability and has been applied to battery stack parameter optimisation [11], CT image location detection [12], and other practical problems. e specific algorithm model includes the producer, scrounger, and guard.…”

“…(9) end for (10) for each producer j � (N * PD) + 1 to N do (11) Update the location of the scroungers via ( 8). (12) end for (13) Use the elite reverse strategy to reverse solution and update outstanding individual via ( 23) and ( 24 erefore, in the process of type label assignment, the same fault types at different motor speeds and loads under the background of signal data were given the same type label. According to the standard, ten faults with different severities in the rolling element, inner raceway, and outer raceway of the driving-end rolling bearing were studied for sensors with different sources on the driving-end and fan-end bearing seats.…”

Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

“…SSA is seen to have good performance in diverse search spaces. The SSA has been recently employed in various applications, including but not limited to: optimal model parameters identification of the Proton Exchange Membrane Fuel Cell (PEMFC) [46], optimal brain tumor diagnosis [47], pulmonary nodule detection [48], carbon price forecasting [49] and 3D route planning for UAVs [50].…”

A Fusion Crossover Mutation Sparrow Search Algorithm

“…Lei et al [25] introduced the Levy flight strategy into SSA, which improved the global optimization ability of SSA, but increased the complexity. Zhang et al [26] used the sine cosine algorithm as a hybrid algorithm to help SSA jump out of local optimum. Although all the above various improvements contribute to optimization performance, there are still certain shortcomings, and thus optimization performance can be further improved.…”