# A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection
## 6 Related Works  
During recent years, with the growing interest on deep learning methods [26; 27; 28; 29; 30] and wireless sensor networks [31; 32; 33; 34; 35], the feature selection methods are drawing more and more research attention. The existing feature selection methods could be classified into two types, namely, batch methods and online methods. Based on the selection criterion choice, the batch methods can be roughly divided into three categories: filter, wrapper and embedded methods. Filter methods [36] are in independent of any earning algorithm, and relies on evaluation measures such as correlation, dependency, consistency, distance and information to select features. A wrapper method [6] performs a forward or backward strategy in the space of all possible feature subsets, using a classifier of choice \begin{table}
\begin{tabular}{c c c c c c} \hline  & **5** & **10** & **20** & **30** & **40** & **80** \\ \hline Original & 62.1 \(\pm\) 0.2 & 72.3 \(\pm\) 0.1 & 92.5 & 92.3 & 92.3 & 92.3 \\ Wind-1000 & 60.4 \(\pm\) 1.0 & 70.9 \(\pm\) 0.4 & 86.1 \(\pm\) 0.2 & 88.3 \(\pm\) 0.1 & 89.2 & 89.3 \\ Wind-5000 & 58.9 \(\pm\) 2.1 & 69.1 \(\pm\) 0.9 & 85.2 \(\pm\) 0.5 & 87.3 \(\pm\) 0.1 & 88.4 \(\pm\) 0.1 & 88.6 \\ \hline \end{tabular}
\end{table} 
Table 1: The impact of the number of selected features to the detection accuracy. The first row indicates the quantity of features we selected from our three datasets. \begin{table}
\begin{tabular}{c c c} \hline  & **Our Method** & **OPCA** \\ \hline original & 9.4 & 12.4 \\ wind-1000 & 16.7 \(\pm\) 0.2 & 14.1 \\ wind-5000 & 18.3 \(\pm\) 0.6 & 16.4 \\ \hline \end{tabular}
\end{table} 
Table 2: The comparison of our method with the OPCA method on false positive rate. \begin{table}
\begin{tabular}{c c c} \hline  & **Our Method** & **OPCA** \\ \hline original & 22.4 \(\pm\) 0.2 & 54.1 \\ wind-1000 & 28.7 \(\pm\) 0.5 & 60.6 \\ wind-5000 & 31.3 \(\pm\) 1.2 & 65.2 \\ \hline \end{tabular}
\end{table} 
Table 3: The comparison of our method with the OPCA (Online Principal Component Analysis) method on false negative rate. to assess each subset. Generally, this method has high accuracy and can search the feature suitable for the predetermined learning algorithm, but the exponential number of possible subsets makes the method computationally expensive. The embedded methods [37] attempt to simultaneously maximize classification performance and minimize the number of features used based on a classification or regression model with specific penalties on coefficients of features. The embedded methods aim to integrate feature selection process into model training process. It can provide suitable feature subset for the learning algorithm much faster than the wrapper methods, but the selected features may be not suitable for other learning algorithms. In spite of the massive research on feature selection, most of them need a pre-learning process, which cannot be easily scalable for high dimensional data analytics that require dynamic feature selection. Hence, many online feature selection methods have been proposed, which can be classified into two research lines. One assumes that the number of features is fixed while the number of data points changes over time [17]. [PERSON] et al. [18] proposed an Online Feature Selection (OFS) method, which assumes data instances are sequentially presented, and performs feature selection upon each data instance's arrival. [PERSON] et al. [19] presented a simple but smart second-order online feature selection algorithm that is extremely efficient, scalable to large scale and ultra-high dimensionality. The other online assumes that the number of data instances is fixed while the number of feature changes over time. [PERSON] et al. [20] firstly proposed the Grafting algorithm based on a stage wise gradient descent approach for this kind of online feature selection.