FCBF算法相较于MRMR算法的优势
时间: 2023-12-10 13:35:46 浏览: 51
根据引用[1]和引用的内容,FCBF算法相较于MRMR算法的优势主要有以下几点:
1. FCBF算法特征选择过程中,不仅考虑了特征与分类之间的相关性,还考虑了特征之间的冗余性,从而能够更加准确地选择出最具代表性的特征。
2. FCBF算法在计算特征相关性时,使用了SU(Symmetrical Uncertainty)代替了MRMR算法中使用的MI(Maximum Relevance Minimum Redundancy)方法,SU方法能够更好地处理特征之间的相关性,从而提高了特征选择的准确性。
3. FCBF算法在计算特征相关性时,使用了一种快速的算法,能够在较短的时间内完成特征选择,因此具有更高的效率。
相关问题
fcbf算法归一化互信息
FCBF算法中的归一化互信息是用来评估特征之间的相关性和冗余性的指标。归一化互信息度量了特征f和已知特征S之间的冗余度,并通过惩罚因子来减少对可取值较多的属性的偏好。
以下是FCBF算法中归一化互信息的计算公式:
```
NMI(S, f) = (I(S; f) - I(S; C)) / H(S)
```
其中,NMI(S, f)表示特征f和已知特征S之间的归一化互信息,I(S; f)表示特征f和已知特征S之间的互信息,I(S; C)表示已知特征S和类别C之间的互信息,H(S)表示已知特征S的熵。
通过计算归一化互信息,可以判断特征f是否冗余。如果NMI(S, f)小于NMI(C, f),则特征f是冗余的;反之,如果NMI(S, f)大于NMI(C, f),则特征f是重要的。
粒子群算法优化支持向量机英文
粒子群算法优化支持向量机的英文表达为"Particle Swarm Optimization (PSO) for optimizing Support Vector Machines (SVM)". \[1\] PSO is a population-based stochastic optimization technique that is similar to other evolutionary computation (EC) techniques such as Genetic Algorithms (GA). These techniques are based on population and utilize a fitness function to evaluate the individuals in the population. They all update the population and search for the optimal solution using random techniques. However, unlike EC and GA techniques, Particle Swarm Optimization does not have genetic operators such as crossover and mutation. Instead, particles are updated based on their internal velocities. Additionally, the information sharing mechanism in Particle Swarm Optimization is different from other EC algorithms. In EC, chromosomes share information with each other, so the entire population moves towards an optimal region as a collective. But in PSO, only the best particle shares information with others. Particle Swarm Optimization is a highly effective algorithm for solving real-valued global optimization problems and is suitable for large-scale research. \[1\] PSO is inspired by the collective behavior of insects, herds, flocks, and schools, where these groups cooperate to search for food and each member of the group continuously changes its search pattern based on its own experience and the experience of other members. \[2\] The PSO algorithm determines the optimal values of FAMNN parameters using Particle Swarm Optimization, which was first proposed by Kennedy and Eberhart in 1995. \[3\] The algorithm is an evolutionary technique that draws inspiration from the social behavior of bird flocks or fish schools and simulates the properties of particles in a swarm. Each particle in the PSO system changes its position (state) over time. In the PSO system, particles fly around a multidimensional search space. During the flight, each particle adjusts its position based on its own experience and the best positions encountered by itself and its neighbors. In this algorithm, each particle has a velocity and a position. \[3\]
#### 引用[.reference_title]
- *1* *3* [基于FCBF特征选择和粒子群优化的模糊神经网络视听情感识别翻译](https://blog.csdn.net/qq_41534566/article/details/86024200)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [Python实现PSO粒子群优化支持向量机分类模型(svc算法)项目实战](https://blog.csdn.net/weixin_42163563/article/details/131245865)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
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