# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 3 Detection of CISs Based on Traditional Machine Learning Methods
### Feature Classification  
During the candidate region segmentation and feature extraction, the research object should be extracted as much as feasible, regardless of the amount of false positives created. After feature extraction for all candidate regions segmented, a classification method needs to be implemented to separate the true lesion regions from the fake ones, which entails conducting false positive elimination. For the detection of lung CT signs, researchers usually adopt the feature classifier method; that is, they calculate the image features of the discernible target region and false positive region from the candidate region, carry out feature selection, and the candidate regions are classified using a discriminative classification model or combinatorial classifier. While blindly adding features is not effective in improving model performance, based on the hypothesis that different types of features may contain complementary information, researchers have conducted a lot of research on how to select suitable features to achieve better classification performance, and have proposed different feature selection algorithms. [PERSON] [25] and [PERSON] [24] refined the traditional genetic optimization feature selection algorithm to improve the classification performance of benign and malignant lung nodules by selecting the optimal subset of features for identifying CISs from the numerous graphical feature sets of lung nodules. [PERSON] [26] proposed a feature selection method based on minimum spanning trees, which considered the relationship between different features on the basis of the traditional method and captured the relationship between the discriminative power of different features, so as to select the most important features for sign recognition. The feature selection technique was tested on 511 actual CT images with a classification accuracy of 91.96%. This approach is a significant advance in feature selection methods, breaking away from the traditional genetic algorithms, and provides a new route for research into improved feature selection algorithms. In the classification stage, the choice of classification algorithm plays a crucial role in the recognition result. Common classifiers include support vector machine (SVM), decision tree (DT), Bayesian classifiers, K-nearest neighbor (KNN), linear discriminant classifier, etc. Although tremendous progress has been achieved in the performance of traditional single classifiers, the performance of single classifiers in practical applications is still unsatisfactory. Since the fusion of many features may compensate for the lack of a single feature, the application of fusing multiple classifiers to compensate for the limitations of a single classifier in classification seems to be a promising approach. Current classification fusion work may be classified into two groups: soft classification fusion and hard classification fusion. The core of these soft classification fusion approaches is accomplished by weighing the outputs of each classifier and assigning each classifier a weight (between 0 and 1). The weight is defined by the training performance of the classifier on the data, and the output of the classifier is the weighted result of the predicted probability of different classifiers on the category to make the final judgment. The key of this strategy lies in how to assign weights to the classifiers. [PERSON] [27] merged matrix and classification confidence value to calculate the weight assigned to each classifier, and completely analyzed the previous performance of the classifier for data training and the features of the present data. The classification accuracy achieved 76.79%, which may greatly enhance the classification performance of signs compared to a single classifier (SVM: 72.02%, KNN: 70.94%, DT: 68.38%).