# Ensemble of Networks for Multilabel Classification
## 3 DataSets  
The following multilabel data sets were selected to evaluate our approach. These are standard benchmarks in multilabel research and run the gamut of typical multilabel problems (music, image, biomedical, and drug classifications). The names provided below are not necessarily those reported in the original papers but rather those commonly used in the literature. 1. Cal500 [55]: This dataset contains human-generated annotations, which label some popular Western music tracks. Tracks were composed by 500 artists. Cal500 has 502 instances, including 68 numeric features and 174 unique labels. 2. Scene [11]: This dataset contains 2407 color images. It includes a predefined training and testing set. The images can have the following labels: beach (369), sunset (364), fall foliage (360), field (327), mountain (223), and urban (210). Sixty-three images have been assigned two category labels and one image three, making the total number of labels fifteen. The images all went through a preprocessing procedure. First, the images were converted to the CIE Luv space, which is perceptually uniform (close to Euclidean distances). Second, the images were divided into a 7 \(\times\) 7 grid, which produced 49 blocks. Third, the mean and variance of each band were computed. The mean represents a low-resolution image, while the variance represents computationally inexpensive texture features. Finally, the images were transformed into a feature vector (49 \(\times\) 3 \(\times\) 2 = 294 dimensions). 3. Image [56]: This dataset contains 2000 natural scene images. Images are divided into five base categories: desert (340 images), mountains (268 images), sea (341 images), sunset (216 images), and trees (378 images). Categorizing images into these five basic types produced a large dataset of images that belonged to two categories (442 images) and a smaller set that belonged to three categories (15 images). The total number of labels in this set, however, is 20 due to the joint categories. All images went through similar preprocessing methods as discussed in [11]. 4. Yeast [57]: This dataset contains biological data. In total there are 2417 micro-array expression data and phylogenetic profiles. They are represented by 103 features and are classified into 14 classes based on function. A gene can be classified into more than one class. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline  & [47] & [48] & [49] & [50] & [2] & [51] & [52] & [53] & [54] & [41] & [9] & **Here** \\ \hline Ensemble & & & & & X & & & & X & X & X \\ GRU & & & & X & & X & X & X & X & & X \\ CNN & & X & X & & X & X & & & & & X \\ LSTM & & & & & X & X & & & & & X \\ TCN & & & & & & & & X & & & & X \\ RNN & & & & & & & & X & & & \\ Inception model & & & & & & & & & X & \\ Random forest & X & & & & & & & & X & & X \\ Gradient descend & X & & & & & X & & & & X & \\ Stochastic gradient descend & & & & & X & X & & & & & \\ Data augmentation & & X & & X & & X & & & X & & \\ Multiple loss functions & & & & & & & X & & & & \\ Binary cross entropy (BCE) loss & & X & & & & & X & X & & & X \\ Multiclass cross entropy loss & & & X & & & & & X & & & \\ Logistic regression & & & & & & & X & & & & \\ Multiple linear regression & & & & & X & & & & & & \\ Quantile regression & & & & & & & & & & & X \\ Various optimization methods & X & & & & & & & & & & \\ Adagrad optimization & & X & & & & & & & & & \\ Variants of Adam optimization & & & & & & & X & X & & & X \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of existing deep learning studies. An X indicates whether a particular technique or model was used. Arts [5]: This dataset contains 5000 art images, which are described by a total of 462 numeric features. Each image can be classified into 26 classes. * Liu [15]: This dataset contains drug data used to predict side effects. In total it has 832 compounds. They are represented by 2892 features and 1385 labels. * ATC [58]: This dataset contains 3883 ATC coded pharmaceuticals. Each sample is represented by 42 features and 14 classes.