[英]One-class Classification
I have more than 2500 samples on which static analysis has been performed, with more than 300 features extracted per sample.我有 2500 多个样本进行了静态分析,每个样本提取了 300 多个特征。
Among these samples, I have discriminated more than 10 APT
class and my aim is to build, for each class, a one-class classifier.在这些样本中,我区分了 10 多个
APT
类,我的目标是为每个类构建一个单类分类器。
I'm using python scikit library for machine-learning, and in particular i'm facing with One-class SVM.我正在使用 python scikit 库进行机器学习,特别是我面临着一类 SVM。
First question: There exist some other good one-class classifier for this approach?第一个问题:这种方法还有其他一些好的单类分类器吗?
Second question: I have to come up with some metrics that can define a sort of "accuracy" of the classifier.第二个问题:我必须提出一些可以定义分类器“准确性”的指标。 Now I know that for one-class SVM the accuracy concept is not so well-define.
现在我知道对于一类 SVM 来说,准确度概念并不是那么明确。 I report my code and my concept:
我报告我的代码和我的概念:
import numpy as np
import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split
df = pd.read_csv('features_labeled_apt17.csv')
X = df.ix[:,1:341].values
X_train, X_test = train_test_split(X,test_size = 0.3,random_state = 42)
clf = svm.OneClassSVM(nu=0.1,kernel = "linear", gamma =0.1)
y_score = clf.fit(X_train)
pred = clf.predict(X_test)
print(pred)
These represents the output of the code:这些代表代码的输出:
[ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 -1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 -1 1 1 1
1 1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1]
The 1 represent of course the well-labeled sample, while the -1 represent the wrong one. 1当然代表标记良好的样本,而-1代表错误的样本。
First: do you think this can be a good approach?第一:你认为这是一个好方法吗? Second: For metrics, if I divide the total element in the testing set by the wrong labeled?
第二:对于指标,如果我将测试集中的总元素除以错误的标签?
In my understanding in machine learning algorithms, your use case is not a good one to apply oneclass-SVM classifier.根据我对机器学习算法的理解,您的用例不是应用 oneclass-SVM 分类器的好用例。
Normally, oneclass-svm is used for Unsupervised Outlier Detection problems.通常,oneclass-svm 用于无监督的异常值检测问题。 Refer this page to see the implementation of oneclass-svm to detect outliers.
请参阅此页面以查看 oneclass-svm 检测异常值的实现。
Just display your data-frame, I will find any new approach to solve your problem.只需显示您的数据框,我就会找到解决您问题的任何新方法。
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