r - 如何使用遗传算法优化knn中的参数k

Question

我尝试使用r中的遗传算法来优化knn中的参数k。我使用以下代码尝试了此操作，但仍然收到错误。
我根据所选的k值将knn的精度用作适应度函数。如果您了解knn和遗传算法，请帮助我。这是我所做的。

 library(caret)
 library(GA)
 library(class)

#data import 
tea_jenis_F3 <- read.csv("D:/inggrit/program/F3.csv")
str(tea_jenis_F3)

#to check missing data 
anyNA(tea_jenis_F3)

#data slicing
set.seed(101)
intrain_jenis_F3 <- createDataPartition(tea_jenis_F3$category, p= 0.7, list = FALSE)
training_jenis_F3 <- tea_jenis_F3 [intrain_jenis_F3,]
testing_jenis_F3 <- tea_jenis_F3 [-intrain_jenis_F3,]

#transforming the dependent variable to a factor 
training_jenis_F3[["category"]] = factor(training_jenis_F3[["category"]])

#fitness function
fitness_KNN <- function(chromosome)
{
  # First values in chromosome are 'k' of 'knn' method
  tuneGrid <- data.frame(k=chromosome[1])


  # train control
  train_control <- trainControl(method = "cv",number = 10)

  # train the model
  set.seed(1234)
  model <- train(category ~ ., data= training_jenis_F3, trControl=train_control, 
                 method="knn", tuneGrid=tuneGrid)

  # Extract accuracy statistics
  accuracy_val <- model$results$accuracy

}


GA <- ga(type = "real-valued", fitness = fitness_KNN, lower = -10, upper = 10, monitor = NULL)

错误：

Something is wrong; all the Accuracy metric values are missing:
Accuracy       Kappa    
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :1     NA's   :1    
Error: Stopping
In addition: There were 11 warnings (use warnings() to see them)

如果您能帮助我，我将不胜感激。谢谢

Answer 1

我认为问题不在于您的代码，而在于方法：使用遗传算法在此设置中优化k是不可能的，也是没有必要的。

您称为ga(type = "real-valued", lower = -10, upper = 10, ...)，这意味着ga将在-10和10之间搜索最佳值。现在有两个问题：


的负值不适用于knn
k将产生非整数值，例如k为1.234，这当然也是不可能的


幸运的是，在这种情况下，不必使用诸如遗传算法之类的复杂方法。如果要在[1，10]范围内找到最佳ga，只需为每个值计算模型，如下所示：

k_cands <- 1:10
accuracy <- numeric()

for(k in k_cands) {
  [compute model with k]
  accuracy <- c(accuracy, model$results$accuracy)
}

best_k <- k_cands[which.max(accuracy)]