Multi-label Classification with Feature-aware Cost-sensitive Label Embedding

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Multi-label Classification with Feature-aware Cost-sensitive Label Embedding

Hsien-Chun Chiu

Department of CSIE, National Taiwan University Taipei, Taiwan

Hsuan-Tien Lin

Department of CSIE, National Taiwan University Taipei, Taiwan

Abstract—Multi-label classification (MLC) is an important learning problem where each instance is annotated with multiple labels. Label embedding (LE) is an important family of methods for MLC that extracts and utilizes the latent structure of labels towards better performance. Within the family, feature-aware LE methods, which jointly consider the feature and label information during extraction, have been shown to reach better performance than feature-unaware ones. Nevertheless, current feature-aware LE methods are not designed to flexibly adapt to different evaluation criteria. In this work, we propose a novel feature- aware LE method that takes the desired evaluation criterion (cost) into account during training. The method, named Feature- aware Cost-sensitive Label Embedding (FaCLE), encodes the criterion into the distance between embedded vectors with a deep Siamese network. The feature-aware characteristic of FaCLE is achieved with a loss function that jointly considers the embedding error and the feature-to-embedding error. Moreover, FaCLE is coupled with an additional-bit trick to deal with the possibly asymmetric criteria. Experiment results across different data sets and evaluation criteria demonstrate that FaCLE is superior to other state-of-the-art feature-aware LE methods and competitive to cost-sensitive LE methods.

Index Terms—multi-label classification, feature-aware, cost- sensitive, label embedding


In traditional single-label learning tasks, e.g., binary and multi-class classification, one instance is associated with a single label. But in many real-world applications, one given instance is associated with a set of labels. Such a learning task is called multi-label classification (MLC). For example, for image annotation [1], [2], an image usually contains abundant semantic information such as characters, scenes, objects, actions, and colors; for document classification [3], a news article may cover numerous topics such as society, sports, entertainment, international events, and weather.

A straightforward MLC algorithm called binary relevance (BR) [4] transforms the MLC problem into binary classifica- tion sub-problems, one for each label. The binary classifier within each sub-problem of BR is trained independently, without exploiting the joint relationship across different labels, which limits BR’s effectiveness in practice.

To deal with MLC problems more effectively, many Label Embedding (LE) methods have been proposed [5], [6]. LE methods aim to compute an embedding space that captures the relationship of labels. Then, the MLC problem can be

reduced to a regression problem from the feature vector of an instance to the embedded vector in the embedding space.

During the testing stage of LE methods, predictions are first performed in the embedding space and then decoded back to the original label space. The embedding space allows a better representation of the labels based on their relationship, therefore allowing many LE methods to perform better than the plain BR algorithm [5], [7].

One key design in modern LE methods is to consider whether the embedding space is easily “learnable”—i.e., whether the relationship between the feature vector and the embedded vector can be easily captured by a regressor. Such LE methods are called feature-aware, which take the feature information into consideration when learning the embedding space [7]–[9]. By taking both feature information and label relationship into account, feature-aware LE methods have gen- erally reached better performance than feature-unaware ones.

For instance, End-to-End Feature-aware label space Encoding (E2FE) [9] is a state-of-the-art feature-aware LE method that jointly maximizes the recoverability of the label space and the predictability of the embedding space, when the two spaces are connected by a linear decoding matrix. E2FE exhibits superior performance over other LE methods when being extended with the kernel trick and eigenvalue-boosted decoding matrix. But those extension tricks make E2FE time-consuming in practice.

Most of the LE methods are not designed to flexibly adapt to different evaluation criteria, and would incur bad performance if the MLC problems are evaluated by the criterion different from what the methods optimize on. For example, in the objec- tive function of E2FE, every label is considered independently, which indicates that E2FE focuses on Hamming Loss. When evaluated with other losses that are very different from the Hamming Loss, E2FE could then suffer from unsatisfactory performance.

MLC problems that require the methods to take the criterion (cost information) into account are called cost-sensitive MLC (CSMLC) problems [10], [11]. One state-of-the-art CSMLC method is Cost-sensitive Label Embedding with Multidimen- sional Scaling (CLEMS) [11]. CLEMS adapts multidimen- sional scaling (MDS), a classic non-linear manifold learning approach, to embed the cost information as the distance measure within the embedding space. In the testing stage, CLEMS decodes every prediction as the corresponding label


vector of the nearest neighbor within a predefined candidate set. Besides, CLEMS uses a mirroring trick to solve the asymmetric cost problem, which means the costs of predicting one label-set as another and that of the reversed operation are different. CLEMS is shown to reach superior performance on CSMLC problems. Nevertheless, CLEMS maintains a dissimilarity matrix, whose size is quadratic to the size of the candidate set. The matrix results in computational difficulties for larger data sets. Moreover, CLEMS is not feature-aware and thus it is not clear whether the embedding space is easily learnable by the regressors.

In this paper we improve CLEMS by proposing a novel Feature-aware Cost-sensitive Label Embedding (FaCLE) method for CSMLC problems. FaCLE utilizes a deep Siamese network along with a sampling method of label vectors to embed the cost information as the distance between embedded vectors. The nature of sampling and deep learning structures mitigates the computation burdens within CLEMS. The asym- metric cost problem is carefully resolved with an additional- bit trick during training. Furthermore, with a feature-aware component, FaCLE successfully associates the feature space and the embedding space by jointly optimizing the embedding loss and the regression loss, and becomes the first feature- aware cost-sensitive MLC method as far as we know. The experiment results across different data sets and evaluation criteria reveal that FaCLE is superior to the state-of-the-art feature-aware LE method and competitive to cost-sensitive LE method.


Multi-label classification problems have attracted much in- terest in research. The simplest solution is binary relevance [4], which simply divides MLC into independent binary classifica- tion sub-problems for each label. Because of not considering other labels while learning on each label, BR is denounced as lacking the ability to leverage the latent structure between labels and thus reaching unsatisfactory performance.

Label embedding methods, an important family of meth- ods for MLC, are proposed to address the problem of BR [5], [12], [13]. LE methods extract the information across different labels to learn a label embedding space, which is claimed to capture the latent label correlations. Besides, with an embedding dimension smaller than the input dimension, multi-dimensional predictions can be made in the embedding space with lower computation cost but possibly better overall performance. One example is principal label space trans- formation (PLST) [5], which leverages principal component analysis to find the most informative principal dimensions as the embedding dimensions, and decodes the predictions by a linear matrix.

Some LE methods take the feature information into con- sideration while learning the label embedding space [7]–[9], which are therefore called feature-aware LE methods. Feature- aware LE methods can appropriately associate the feature vectors and the label vectors while learning the embedding, making the embedding space more learnable for regressors,

and thus improve the performance. Take CPLST [7], the conditional version of PLST, as an example. Inspired by canonical correlation analysis [14], CPLST uses singular value decomposition to jointly minimize the embedding error and the prediction error, and obtains the feature-aware embedding space. CPLST is reported to be superior to PLST, because the embedding error and the prediction error are optimized jointly instead of separately, making the embedding space more learnable in practice.

Unlike the analytical models like PLST and CPLST, C2AE [15] is the first label embedding model constructed by deep neural networks. By integrating the architectures of deep canonical correlation analysis and auto-encoder, C2AE jointly minimizes the embedding error of the auto-encoder and the regression error of canonical correlation analysis. In addition, C2AE proposes a label-correlation sensitive loss function to better decode the predictions and achieve state-of-the-art performance.

Although existing LE methods demonstrate promising per- formance for MLC problems, most of them are constructed to optimize on only one specific or few evaluation criteria. For example, in C2AE the label-correlation sensitive loss function is computed in a pairwise form of positive labels and negative labels, which is identical to Rank Loss. When encountering different evaluation criteria, those methods may reach bad performance. As a result, cost-sensitive MLC methods, which take the cost information (evaluation criteria) into account during training or testing, have become more important in recent days [10], [11], [16]. For example, Probabilistic Clas- sifier Chain (PCC) [16] is proposed to make Bayes-optimal inference by estimating the probability of each label for the target criterion. But if there is no efficient inference rule designed for the criterion, PCC will encounter computational difficulties.

One particular LE method for CSMLC is Cost-sensitive Label Embedding with Multidimensional Scaling (CLEMS) [11], a state-of-the-art CSMLC method. CLEMS preserves the cost information in the distance of the embedded space by multidimensional scaling (MDS), and to decode every prediction as the nearest neighbor. Although CLEMS is re- ported to reach outstanding performance, CLEMS demands a dissimilarity matrix to be computed beforehand, whose size is quadratic to the number of unique label vectors in training data. Therefore, when handing large data sets, CLEMS could be computationally challenging as well.

In summary, current CSMLC methods can suffer from computational issues, and none of them are feature-aware.

We address the two issues by proposing Feature-aware Cost- sensitive Label Embedding (FaCLE), which utilizes deep Siamese network to keep cost information as the distance of the embedded vectors, and exploits a feature-aware component to jointly optimize the embedding loss and the regression loss.

Moreover, the usage of sampling method and the nature of deep learning structure make FaCLE more feasible and flexible to handle large data sets. The detail of FaCLE will be described in the following section.



Denote X ∈ RN ×d as the d dimensional training feature matrix and Y ∈ {0, 1}N ×kas the k dimensional corresponding label matrix, with N instances and the i-th row being xTi and yTi , where xiis the feature vector and yi is the corresponding label vector. Observing a data set D = (X, Y ), multi-label classification problems aim to learn a model which can make a proper prediction ˆy of a testing instance ˆx.

In order to tackle the given evaluation criterion c of MLC di- rectly, cost-sensitive multi-label classification (CSMLC) meth- ods strive to leverage the information of the evaluation crite- rion in either the training stage or the testing stage. One pre- cursor is cost-sensitive label embedding with multidimensional scaling (CLEMS) [11]. CLEMS utilizes multidimensional scaling to approximate the cost information with the distances of the embedded label vectors. In CLEMS, first a candidate set of label vectors P is decided as the set of label vectors appearing in the training instances. Then a dissimilarity matrix Φ is computed whose elements Φi,j = δ(c(yi, yj)), with yi

and yj being the i-th and j-th vector in P and δ denoting the monotonic function. As the main step, MDS is leveraged to determine the embedded vectors u with an embedding dimension m for vectors in P by iteratively minimizing the stress:

stress =X


Wi,j(∆(ui, uj) − Φi,j)2 (1)

where W and ∆ denote the weight of label pairs and the dis- tance function respectively. After that, an additional regressor is trained with feature vectors and embedded label vectors.

In the testing stage, CLEMS easily decodes a prediction as the corresponding label of its nearest neighbor within embed- ding space. Besides, MDS requires the dissimilarity matrix to be symmetric, but some criteria are asymmetric, which means c(yi, yj) 6= c(yj, yi). As a result, CLEMS proposes a mirroring trick, which views the label vectors as two roles, the ground truth role and the prediction role, and computes a symmetric dissimilarity matrix by considering those 2|P | label vectors. Then the regressor is trained on the truth vectors and the prediction is decoded on prediction vectors.

CLEMS is reported to be significantly better than a wide variety of state-of-the-art CSMLC methods. But one critical drawback of CLEMS is that a dissimilarity matrix needs to be computed beforehand, whose time complexity is O(|P |2), making it infeasible to be applied on large data sets. Another problem is that CLEMS does not consider feature information in training time, making the embedding space usually hard to be learned by the regressor in practice.

Motivated by CLEMS and recent developments of deep learning methods, we propose Feature-aware Cost-sensitive Label Embedding (FaCLE), which utilizes Siamese network to preserve the cost information as the distances between embedded vectors. In our method, only an assigned portion of label costs need to be computed in advance, making training on large data sets more feasible and flexible. Furthermore, we





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Fig. 1. The training architecture of FaCLE. The blue part learns a nonlinear label embedding function Fe by Siamese network, and the green part is the feature-aware component, which learns a nonlinear feature transformation function Fxmaking the training process also considering feature information.

design a feature-aware component to take the feature informa- tion into consideration during the training stage, regularizing the embedding space to be more tractable for the regressor.

As depicted in Figure 1, the overall training architecture of FaCLE comprises two parts: deep cost-sensitive label embedding (the blue part) and feature-aware component (the green part). The former learns a label embedding function Fe by Siamese network such that the distance of each embedded label pairs is monotonic to their cost, and the latter learns a feature transformation function Fxwhich associates the feature space with the label embedding space. Therefore, we formulate the objective function of FaCLE as:

LF aCLE(Fe, Fx) = min

Fe,FxLembed(Fe) + α Lregress(Fe, Fx) (2) where Lembed, Lregress, and α denote the embedding loss, the regression loss, and the balancing parameter respectively. It is worth noticing that FaCLE jointly optimizes the embedding loss and the regression loss instead of separately. The details will be discussed in the following two subsections.

A. Deep Cost-sensitive Label Embedding

Siamese network is a special learning structure which has been exploited to learn decent representations in many research problems [17], [18]. With shared weights in the twin networks, Siamese network is usually aligned with the contrastive loss to enlarge the distance of similar pairs and reduce that of dissimilar pairs.

Our method starts from an idea that label pairs can be the inputs and the cost information of them can be regarded as the similarity measure in Siamese network to learn a cost- sensitive label embedding. Therefore, we propose a kind of restricted version of contrastive loss such that we not merely increase/decrease the distance of dissimilar/similar pairs, but force the distance close to their similarity measure as much as possible. Consequently, we formulate the embedding loss,


which is the same as the stress in CLEMS, as follows:

Lembed(Fe) = X


(∆(Fe(yi), Fe(yj)) − δ(c(yi, yj)))2 (3) But we can notice two problems. First, it is infeasible to optimize on all the label pairs, especially on large data sets.

Accordingly, we suggest simply using random sampling for training label pairs, which give us the feasibility and the flexibility to adjust the training time according to how many computation resources we have. Second, the asymmetric cost problem, which is also discussed in CLEMS. The asymmetric cost, e.g., the rank loss, implies c(yi, yj) 6= c(yj, yi) with the same input pairs (yi, yj) = (yj, yi) in Siamese network’s view.

One intuitive solution is to distinguish the label pairs as two different roles, the ground truth and the prediction. Thus, we propose an Additional-Bit trick (AB-trick), which appends an additional bit 0 to label vectors serving as the ground truth role and an additional bit 1 to label vectors serving as the prediction role. In addition, we both append 0.5 while dealing with symmetric cost to avoid overfitting on the meaningless additional bit. The embedding loss is now modified as:

Lembed(Fe) = X


(∆(Fe(y(t)i ), Fe(y(p)j ))−δ(c(yi, yj)))2 (4) where(t),(p), and S denote the truth role, the prediction role, and the sampling respectively.

B. Feature-aware Component

Feature-awareness has been proved effective in label embed- ding methods [7]–[9]. Empirically, the label embedding space usually becomes intractable for the regressor if it is trained independently. As a consequence, we regularize our deep cost- sensitive label embedding by allying Fe(yi(t)) to the feature transformation Fx(xi) in training time. The reason why we ally Fe(y(t)i ) instead of Fe(y(p)i ) is that the regressor will be trained with the former. Moreover, it is worthwhile to mention that the feature transformation function Fx can also be used as an end-to-end regressor. The resulting regression loss is formulated as:

Lregress(Fe, Fx) = X


∆(Fx(xi), Fe(y(t)i ))2 (5) Until now, we have introduced the complete training struc- ture of FaCLE. Once the training of FaCLE is accomplished, we either easily apply Fx as the regressor r or train a new one with input (X, Z), where Z = Fe(Y(t)). With a coming test instance ˆx, we first compute its prediction ˆz = r(ˆx), then find its nearest neighbor ˆznbr in embedded candidate set P(e) = Fe(P(p)), and finally decode it as the corresponding label vector ˆy ∈ P . Because of the intuition that the distances in P(e) are monotonic to real costs, ˆy should be a reasonable prediction of ˆx.

To the best of our knowledge, we are the first to apply Siamese network to cost-sensitive label embedding, and the first to design a feature-aware CSMLC method, which are the main contributions of this work. The pseudo code of FaCLE is summarized in Algorithm 1 and Algorithm 2.

Algorithm 1: Training of FaCLE Input: X, Y , δ, c, α, m Output: r, P , P(e)

Decide P as the distinct training label vectors Let D = (X, Y )

Sample S = {(xi, yi, yj)} from D and Y Compute C = {δ(c(yi, yj))} of S Initialize Fe, Fx with m

Train Feand Fxto minimize (2), (4), (5) with S, C, α, and AB-trick

Compute embedded vector set Z = Fe(Y(t))

Apply Fx as regressor r or train a new one with (X, Z) Compute embedded candidate set P(e)= Fe(P(p))

Algorithm 2: Predicting of FaCLE Input: x, r, P , P(e)

Output: ˆy Compute ˆz = r(x)

Find the nearest neighbor ˆznbr∈ P(e) of ˆz

Make prediction ˆy as the corresponding y ∈ P of ˆznbr


To evaluate the performance of FaCLE, we conduct exper- iments on the following real-world data sets: birds, emotions, medical, CAL500, scene, yeast, enron, tmc2007 [19]. The detailed statistics of each data set are listed in Table I. We consider four evaluation criteria frequently used in CSMLC problems: Hamming loss(y, ˆy) = 1k



i=1Jy[i] 6= y[i]ˆ K, Accuracy loss(y, ˆy) = 1 − ||y∩ˆ||y∪ˆy||y||1

1, F 1 loss(y, ˆy) = 1 −


||y||1+||ˆy||1, and Rank loss(y, ˆy) = R(y)1 P


(J ˆy[i] <

ˆ y[j]K +


2J ˆy[i] = ˆy[j]K) where R(y) = |{(i, j )|y[i] > y[j ]}|.

Please notice that Hamming loss, Accuracy loss, and F 1 loss are symmetric, while Rank loss is asymmetric.

All the experiment results are reported as the average of 20 independent runs if not specifically acknowledged. In each run, the data sets are randomly split to 50%, 25%, and 25%

for training, validation, and testing correspondingly. The best parameters of each method are selected by using the validation

data set k d N #distinct labels

birds 19 260 645 133

emotions 6 72 593 27

medical 45 1449 978 94

CAL500 174 68 502 502

scene 6 294 2407 15

yeast 14 103 2417 198

enron 53 1001 1702 753

tmc2007 33 500 28596 1172





part and then used for testing.

For all the methods in our experiments, if not mention specifically, we use random forest implemented in scikit-learn [20] as the regressor, with the maximum depth of trees selected from {5, 10, ..., 35}. For FaCLE, Fe is constructed of 2 fully connected layers with 2 times the input label dimension neurons for each layer, and Fx is constructed of 3 fully connected layers with 10% dropout and 5 times the input feature dimension neurons for each layer. ReLU is deployed as the activation function and the mini-batch size is 10. The learning rate is selected in a range [0.00001, 0.01] and α is fixed as 0.03. We set the sampling number |S| to be 14|P |2, and the distance function ∆ to be L2-norm. Square root function is chosen as the monotonic function δ according to the suggestion of [11]. Additionally, we name the feature-unaware version of FaCLE, which has only the deep cost-sensitive label embedding part, as DCLE.

A. Comparing with Cost-sensitive Label Embedding Methods We first compare DCLE, FaCLE with CLEMS [11], which is introduced in section 3. The embedding dimension m is appointed to be equivalent to k, the dimension of label vectors.

The parameters of CLEMS are selected in the same range within its original paper. The results are illustrated in the Table II.

From the table we can find that DCLE is comparable to CLEMS within the first 7 small data sets, which illustrates the efficient use of only 14|P |2 samples in DCLE comparing to the size of dissimilarity matrix |P |2in CLEMS. Moreover, DCLE performs obviously better than CLEMS on the data set tmc2007, which is much larger than others, supporting the effectiveness of our deep cost-sensitive label embedding method. In addition, according to the results of DCLE and FaCLE, feature-awareness plays a positive role in half of the results (16 of 32), and we can find some data sets not suitable for feature-aware methods. That depends on the difficulty of extracting useful feature information from the data set.

B. Comparing with Feature-aware Label Embedding Methods We further compare FaCLE with other existing feature- aware label embedding based methods. Canonical Correlated Autoencoder (C2AE) [15] is another state-of-the-art feature- aware label embedding method. C2AE derives the embedded vectors by jointly optimizing the embedding loss of an auto- encoder and the regression loss of a deep neural network regressor in a way of canonical correlation analysis. A label- correlation sensitive loss function is also proposed to better recover the prediction to the original label space. Furthermore, C2AE can be easily extended to address the missing label problems. In this experiment, the detailed architecture of C2AE is set to be identical to that in [15].

The embedding dimension m is set to be 0.5*k, and parameters are all selected within the same range referring to the original settings in [15]. Because C2AE is designed for using deep neural network as the regressor, we demonstrate the results of FaCLE directly using feature transformation function

Hamming Loss


birds 0.044 ± 0.001 0.047 ± 0.001 0.046 ± 0.001 emotions 0.193 ± 0.003 0.184 ± 0.003 0.193 ± 0.003 medical 0.024 ± 0.002 0.016 ± 0.001 0.012 ± 0.002 CAL500 0.159 ± 0.001 0.162 ± 0.001 0.159 ± 0.001 scene 0.092 ± 0.003 0.097 ± 0.003 0.096 ± 0.002 yeast 0.193 ± 0.002 0.191 ± 0.001 0.194 ± 0.001 enron 0.042 ± 0.002 0.051 ± 0.000 0.026 ± 0.005 tmc2007 0.052 ± 0.002 0.043 ± 0.000 0.055 ± 0.000

Accuracy Loss


birds 0.391 ± 0.008 0.375 ± 0.006 0.347 ± 0.020 emotions 0.411 ± 0.007 0.421 ± 0.006 0.420 ± 0.006

medical 0.344 ± 0.013 0.241 ± 0.030 0.278 ± 0.030 CAL500 0.729 ± 0.002 0.736 ± 0.002 0.731 ± 0.003 scene 0.268 ± 0.005 0.260 ± 0.003 0.272 ± 0.005 yeast 0.436 ± 0.002 0.440 ± 0.003 0.435 ± 0.003 enron 0.424 ± 0.012 0.535 ± 0.004 0.321 ± 0.052 tmc2007 0.381 ± 0.013 0.274 ± 0.013 0.352 ± 0.009

Rank Loss


birds 0.152 ± 0.005 0.201 ± 0.005 0.205 ± 0.004 emotions 0.203 ± 0.004 0.222 ± 0.007 0.238 ± 0.008 medical 0.114 ± 0.010 0.114 ± 0.008 0.137 ± 0.009 CAL500 0.327 ± 0.001 0.333 ± 0.002 0.349 ± 0.006 scene 0.132 ± 0.002 0.144 ± 0.005 0.148 ± 0.006 yeast 0.217 ± 0.002 0.228 ± 0.001 0.233 ± 0.002 enron 0.132 ± 0.013 0.182 ± 0.001 0.173 ± 0.005 tmc2007 0.124 ± 0.009 0.109 ± 0.001 0.124 ± 0.001

F1 Loss


birds 0.325 ± 0.007 0.333 ± 0.007 0.329 ± 0.007 emotions 0.314 ± 0.004 0.325 ± 0.005 0.323 ± 0.003 medical 0.321 ± 0.014 0.298 ± 0.017 0.312 ± 0.015 CAL500 0.580 ± 0.003 0.592 ± 0.002 0.583 ± 0.002 scene 0.252 ± 0.003 0.248 ± 0.005 0.247 ± 0.005 yeast 0.335 ± 0.003 0.336 ± 0.002 0.337 ± 0.002 enron 0.359 ± 0.008 0.412 ± 0.005 0.236 ± 0.039 tmc2007 0.306 ± 0.017 0.222 ± 0.001 0.274 ± 0.001




Fx as the regressor. Also for avoiding confusion, We denote FaCLE in this experiment as FaCLE 0.5 NN. The results are listed in the Table III

We can find FaCLE 0.5 NN performs mostly better than C2AE, which illustrates the effectiveness of cost-sensitivity and supports our intuition to design a general cost-sensitive label embedding method. Additionally, we can find that on some data sets FaCLE 0.5 NN performs better than FaCLE.

And we especially claim that our model has the flexibility to choose proper regressors for better performance according to each targeted data set.


We propose Feature-aware Cost-sensitive Label Embedding (FaCLE) for multi-label classification problems. By exploiting Siamese network, FaCLE successfully learns a cost-sensitive label embedding where the cost information is kept as the distance measure. With Additional-Bit trick, the asymmetric cost can be also handled by FaCLE. We further design a


Hamming Loss


birds 0.320 ± 0.017 0.045 ± 0.002 emotions 0.627 ± 0.010 0.241 ± 0.007 medical 0.324 ± 0.005 0.021 ± 0.001 CAL500 0.288 ± 0.002 0.158 ± 0.000 scene 0.136 ± 0.002 0.082 ± 0.002 yeast 0.221 ± 0.002 0.195 ± 0.002 enron 0.074 ± 0.001 0.056 ± 0.000 tmc2007 0.052 ± 0.000 0.043 ± 0.000

Accuracy Loss


birds 0.923 ± 0.002 0.304 ± 0.039 emotions 0.683 ± 0.002 0.532 ± 0.011 medical 0.930 ± 0.001 0.221 ± 0.040 CAL500 0.714 ± 0.001 0.745 ± 0.002

scene 0.525 ± 0.004 0.269 ± 0.007 yeast 0.484 ± 0.002 0.451 ± 0.002 enron 0.644 ± 0.003 0.583 ± 0.001 tmc2007 0.350 ± 0.001 0.287 ± 0.000

Rank Loss


birds 0.211 ± 0.006 0.225 ± 0.005 emotions 0.474 ± 0.005 0.356 ± 0.015

medical 0.225 ± 0.002 0.197 ± 0.006 CAL500 0.260 ± 0.000 0.386 ± 0.002

scene 0.256 ± 0.002 0.244 ± 0.010 yeast 0.243 ± 0.001 0.235 ± 0.003 enron 0.191 ± 0.002 0.201 ± 0.000 tmc2007 0.118 ± 0.000 0.095 ± 0.000

F1 Loss


birds 0.875 ± 0.003 0.370 ± 0.006 emotions 0.531 ± 0.002 0.396 ± 0.006 medical 0.871 ± 0.001 0.241 ± 0.031 CAL500 0.559 ± 0.001 0.606 ± 0.001

scene 0.491 ± 0.003 0.245 ± 0.006 yeast 0.368 ± 0.002 0.341 ± 0.002 enron 0.509 ± 0.003 0.331 ± 0.048 tmc2007 0.270 ± 0.001 0.204 ± 0.001




feature-aware component to make FaCLE jointly optimize the embedding loss and the regression loss instead of separately.

With the embedding, FaCLE decodes the predictions to the nearest neighbors within a pre-decided candidate set. The experiment results show that FaCLE achieves decent perfor- mance by efficiently using a small quantity of sampling against other cost-sensitive label embedding method, and the feature- awareness further improves the performance on some data sets.

Moreover, we also demonstrate that FaCLE is superior to other feature-aware label embedding methods, which supports the effectiveness of cost-sensitivity. As far as we know, FaCLE is the first cost-sensitive label embedding method to utilize deep learning structure, and the first feature-aware CSMLC method.


The work arises from the Masters thesis of the first author [21]. We thank Profs. Yu-Chiang Frank Wang, Yun-Nung Chen, the anonymous reviewers and the members of the NTU Computational Learning Lab for valuable suggestions. This

work is partially supported by the Ministry of Science and Technology of Taiwan under number MOST 103-2221-E- 002- 149-MY3.


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