This paper presents an approach of model-oriented road detection based on trapezoidal model proposed by H. Jeong, et al and fuzzy Support Vector Machine (SVM). Firstly, the frames ex-tracted from the video are preproc...This paper presents an approach of model-oriented road detection based on trapezoidal model proposed by H. Jeong, et al and fuzzy Support Vector Machine (SVM). Firstly, the frames ex-tracted from the video are preprocessed by Pulse Coupled Neural Network (PCNN), and then handled by Kalman filter and Expectation Maximization (EM) algorithms. Next, according to the road's dif-ferent feathers, using fuzzy algorithm chooses a corresponding SVM for further lane detection, and then using morphological filters obtains the final detecting result. For different types of roads, this method uses fuzzy algorithm to choose different SVMs. Furthermore, in preprocessing using PCNN removes the shadow in the road to reduce the effect of illumination variations. Experimental results show that our method can receive better lane detecting results than the trapezoidal model and BP proposed by H. Jeong, et al..展开更多
Purpose-Imbalanced learning presents a significant challenge in the field of machine learning.Although traditional support vector machine(SVM)demonstrate relatively robust performance when handling imbalanced datasets...Purpose-Imbalanced learning presents a significant challenge in the field of machine learning.Although traditional support vector machine(SVM)demonstrate relatively robust performance when handling imbalanced datasets,they assign equal learning contributions to all samples,which can lead to decision boundaries that are biased toward the majority class,especially in the presence of outliers or noise.To address this issue,this paper proposes a fuzzy SVM model based on the Hilbert-Schmidt independence criterion(HSIC)heuristic strategy and information entropy(HEFTSVM)for imbalanced learning.Design/methodology/approach-This study introduces an effective fuzzy membership allocation strategy combining HSIC heuristic strategies and information entropy.The fuzzy membership function leverages structural information derived from both the input and feature spaces.Specifically,entropy assesses membership within the input space,whereas HSIC evaluates it in the feature space.The final fuzzy membership function is derived by multiplying the memberships from both spaces.This approach is integrated with the twin support vector machine(TSVM)algorithm to create the HEFTSVM algorithm.We evaluated the model’s effectiveness through comparative experiments on 39 datasets with varying imbalance levels.Findings-Experimental results validate the effectiveness of HEFTSVM in addressing class imbalance classification problems,achieving an average geometric mean(GM)of 86.71%on low-imbalance datasets and 82.13%on high-imbalance datasets.These findings demonstrate that HEFTSVM exhibits better robustness and generalization performance than existing learning models.Originality/value-This study proposes a fuzzy membership degree allocation strategy based on HSIC heuristic and information entropy,effectively addressing the class imbalance issue,mitigating the sensitivity of TSVM to noise and introducing the noise-robust HEFTSVM model.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60671062)the National Basic Research Program of China (2005CB724303)
文摘This paper presents an approach of model-oriented road detection based on trapezoidal model proposed by H. Jeong, et al and fuzzy Support Vector Machine (SVM). Firstly, the frames ex-tracted from the video are preprocessed by Pulse Coupled Neural Network (PCNN), and then handled by Kalman filter and Expectation Maximization (EM) algorithms. Next, according to the road's dif-ferent feathers, using fuzzy algorithm chooses a corresponding SVM for further lane detection, and then using morphological filters obtains the final detecting result. For different types of roads, this method uses fuzzy algorithm to choose different SVMs. Furthermore, in preprocessing using PCNN removes the shadow in the road to reduce the effect of illumination variations. Experimental results show that our method can receive better lane detecting results than the trapezoidal model and BP proposed by H. Jeong, et al..
文摘Purpose-Imbalanced learning presents a significant challenge in the field of machine learning.Although traditional support vector machine(SVM)demonstrate relatively robust performance when handling imbalanced datasets,they assign equal learning contributions to all samples,which can lead to decision boundaries that are biased toward the majority class,especially in the presence of outliers or noise.To address this issue,this paper proposes a fuzzy SVM model based on the Hilbert-Schmidt independence criterion(HSIC)heuristic strategy and information entropy(HEFTSVM)for imbalanced learning.Design/methodology/approach-This study introduces an effective fuzzy membership allocation strategy combining HSIC heuristic strategies and information entropy.The fuzzy membership function leverages structural information derived from both the input and feature spaces.Specifically,entropy assesses membership within the input space,whereas HSIC evaluates it in the feature space.The final fuzzy membership function is derived by multiplying the memberships from both spaces.This approach is integrated with the twin support vector machine(TSVM)algorithm to create the HEFTSVM algorithm.We evaluated the model’s effectiveness through comparative experiments on 39 datasets with varying imbalance levels.Findings-Experimental results validate the effectiveness of HEFTSVM in addressing class imbalance classification problems,achieving an average geometric mean(GM)of 86.71%on low-imbalance datasets and 82.13%on high-imbalance datasets.These findings demonstrate that HEFTSVM exhibits better robustness and generalization performance than existing learning models.Originality/value-This study proposes a fuzzy membership degree allocation strategy based on HSIC heuristic and information entropy,effectively addressing the class imbalance issue,mitigating the sensitivity of TSVM to noise and introducing the noise-robust HEFTSVM model.
