A diagram retrieval method with multi-label learning

Assalamu’alaikum Wr. Wb. and Greetings.I am pleased and honored to welcome you at the First International Conference on Computer Science and Applied Mathematic (ICCSAM 2018), which was held this year by AMIK and STIKOM Tunas Bangsa Pematangsiantar in collaboration with the Indonesian Computer and Information Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS) and Bank Muamalat. As Chair of the Foundation, I would like to convey a greeting Welcome to Parapat City Lake Toba to presenters from various countries and especially those I respect for the presence of scientists in the field of Computer and Mathematical Sciences and industry practitioners in the 2018 ICCSAM event.I see that this activity is designed to enhance the exchange of knowledge and new discoveries in computer science and mathematics and related fields in industry. I hope that scientists in the field of computer science and mathematics and those who work in the industry can share knowledge and work together as a team that has mutual relations with each other. I am very pleased to say that the theme of this conference, “Advancing Computability Innovation” is very much in line with the objectives of AMIK and STIKOM conferences and missions, namely “to become a Science and Technology based Study Program that meets industrial needs and functions as a research center in Information Data Science. The conference was held in response to output and aoutcame which had a significant contribution in the field of computer science and mathematics as an inseparable unit of science towards the development of local and global industries. It is our happiness and honor to welcome distinguished professors present to convey their expertise at this conference.I hope this meeting will enable the development of productive dialogue between participants from various countries. They also provide invaluable opportunities for networking among participants, institutions and industries. I also hope that diversity in these fields can reveal more opportunities for researchers and practitioners from all over the world to start a lot of research related to the industry in the future.I would like to congratulate AMIK and STIKOM Tunas Bangsa for starting this conference with a synergistic contribution from dedicated partners namely the USU Faculty of Mathematics and Natural Sciences, USU’s Faculty of Computer and Information Technology, the Indonesian Computer and Informatics Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS), Bank Muamalat, Malaysia Pahang University (UMP), University of Essex, United Kingdom, Ankara, Turkey and Institute of Applied Mathematics Middle East Technical University to all delegates for their full support, cooperation and contribution to the 2018 ICCSAM. I also want to thank the Organizing Committee for their perseverance and extraordinary efforts. Various sponsors are also rewarded for their contributions. I really hope that all participants will get a pleasant stay here at NIAGARA HOTEL and bring back unforgettable experiences and valuable knowledge from this conference.Thanks.H. Ahmad Ridwansyah Putra

In multi-label learning, research on label correlation provides an effective solution to compress the hypothesis space of classifiers. However, the current works focus on the label correlation adapted to overall data, while ignoring the locally targeted information presented by some instances. The lack exploration on the distribution of local label correlation in multi-label instance space undoubtedly limits the in-depth application of label correlation in multi-label learning. Based on fuzzy rough set theory, the instance-level label correlation distribution is first proposed in this paper and applied to design a novel multi-label learner. For each multi-label instance, the local importance of features to label is quantitatively analyzed, by considering the decisive influence of input information on decision-making. According to coincidence degree between local feature weight distribution for different labels, the instance-level label correlation is constructed. In order to reflect the internal relationship between label variables objectively, the instance-level label correlation distribution is integrated into the empirical label relevance. On the basis, the label relevance matrix is used to define the constraints of optimization function in a new form. The relative position of sub-separating hyperplanes in input space is quantitatively characterized to reduce the complexity of multi-label classifier and improve learning performance. The experiment results on eighteen multi-label datasets illustrate the effectiveness of our algorithm. The impact of core parameters on performance is also dissected.

To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years. Some approaches have been proposed to select label-specific features to utilize discriminate features for multi-label classification. Although label correlation has been considered in learning label-specific features, the critical correlation among instances was less taken into account. In this paper, we proposed a new approach called multi-label learning with label-specific features using correlation information (LSF-CI) to learn label-specific features for each label with the consideration of both correlation information in label space and correlation information in feature space. In the LSF-CI, the instance correlation in feature space is computed by a probabilistic neighborhood graph model, and label correlation in label space is computed by cosine similarity. For multi-label data, the LSF-CI has the capability to select Label-specific features for each label as well as classify an unseen instance into a set of relevant labels. To validate the effectiveness of LSF-CI, we conducted comprehensive experiments on eight multi-label datasets. The experimental results demonstrate that the LSF-CI is capable of selecting compact label-specific features, and achieving a competitive performance in comparison with the performances of the existing multi-label learning approaches.

In multi-label learning with missing labels, previous works usually only consider all features, or only consider label-specific features. It is obviously inappropriate to select a subset of features only considering which have a great discriminability for all labels or for a label. Besides, they are built on an assumption that if two labels are correlated, their regression coefficient vectors should be similar. However, it is hard to hold this assumption in real-world applications. Therefore, we propose a novel multi-label classification approach for missing labels via considering common and label-specific features with correlation information. More specifically, we firstly utilize <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{1}$</tex> - norm regularizer over label correlation matrix, and a new sup-plementary label matrix is augmented from the incomplete label matrix by learning label correlations. Then, for a better label recovery, we introduce <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2,1}$</tex> -norm and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{1}$</tex> -norm regularizers to learn common and label-specific features simultaneously. Thirdly, to make more rational use of label correlation to solve missing labels, we use a regularizer to constrain label correlations on outputs of labels instead of regression coefficient matrix. Finally, an objective function is designed in terms of the above processing. Extensive experiments conducted on eight data sets demonstrate the effectiveness of the proposed approach.

Semi-supervised multi-label feature selection with adaptive structure learning and manifold learning

Multi-modal multi-label learning provides a fundamental framework for complex objects, which can be represented with multiple modalities and annotated with multiple labels simultaneously. Different modalities can usually provide complementary information, which may lead to improved performance. What's more, exploiting label correlations is crucially important to multi-label learning. However, most existing multi-label learning approaches do not sufficiently consider the complementary information among different modalities. In this paper, we propose a novel end-to-end deep learning framework named Rethinking Modal-oriented Label Correlations (RMLC), which sequentially polish the label prediction with each individual modality. In order to explicitly account for the correlated prediction of multiple labels, RMLC leverages an efficient sequential modal-based exploration to rethink label correlations. The final prediction of each label involves the collaboration between modal-specific prediction and the prediction of other labels based on cross-modal interaction. Comprehensive experiments on benchmark datasets validate the effectiveness and competitiveness of the proposed RMLC approach.

On social media, the user generated contents, e.g., Articles and images, can be assigned with multiple labels. In this paper, we focus on the problem of performing multi-label classification on social media data, where the user generated contents are associated with multiple labels. Multi-label learning studies the problem where each object is represented by a single instance and associated with a set of labels. Current multi-label learning algorithms mainly exploit label correlations globally, by assuming that the label correlations are shared by all the examples. In real applications, however, different examples may share different label correlations. In this paper, we propose a Local Pair wise Label Correlation (LPLC) method for social media content categorization. We try to exploit the strongest local pair wise label correlations between the ground truth labels for each training example by computing the maximum conditional probabilities. If two labels have strong correlation, there will be a larger conditional probability of one label given by another. In the training stage, we find the most correlated labels for ground truth labels of each training example. In the test stage, we make prediction through maximizing the posterior probability, which is estimated with the distribution of each label in the k nearest neighbors and their most correlated local pair wise label correlations. We compare our method with six well-established multi-label learning algorithms over nine data sets from different social media data domains and scales. Comparison results with the state-of-the-arts approaches manifest competitive performances of our method.

Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels. Due to the exponential number of possible label sets to be considered by the predictive model, it is commonly assumed that label correlations should be well exploited to design an effective multi-label learning approach. On the other hand, class-imbalance stands as an intrinsic property of multi-label data which significantly affects the generalization performance of the multi-label predictive model. For each class label, the number of training examples with positive labeling assignment is generally much less than those with negative labeling assignment. To deal with the class-imbalance issue for multi-label learning, a simple yet effective class-imbalance aware learning strategy called cross-coupling aggregation (COCOA) is proposed in this article. Specifically, COCOA works by leveraging the exploitation of label correlations as well as the exploration of class-imbalance simultaneously. For each class label, a number of multiclass imbalance learners are induced by randomly coupling with other labels, whose predictions on the unseen instance are aggregated to determine the corresponding labeling relevancy. Extensive experiments on 18 benchmark datasets clearly validate the effectiveness of COCOA against state-of-the-art multi-label learning approaches especially in terms of imbalance-specific evaluation metrics.

Despite great effort in research and teaching as well as in raising women’s quotas in the public sector and in enterprises, a gender gap in computer science can still be observed. The reasons are manifold, including the lack of interest of girls because of existing stereotypes of male nerds, misconceptions in the field of computer science, and concrete differences in students’ self-concept and performance. Girls do not choose schools or studies in the field of computer science because working on computers or coding is unattractive to them. Research shows poorer performance, missing interest, and lower self-concept for girls in secondary computing education as well as a high dropout rate of female students in computer science courses at the bachelor’s level. Software development exams contribute to those high dropout rates, and women often choose a different subject or even stop their university education altogether. This leads to fewer female employees in the field of computer science, and this situation calls for special measures at several levels. The current paper provides an overview of the initiatives of our university that aim to address several aspects of the gender gap. The programs offered aim to (1) increase interest in computer science and recruit (highly) gifted girls for our talent programs, (2) recruit and support female bachelor’s and master’s students in computer science, and (3) redesign programming courses and teaching materials to reduce the gender gap in performance and support, especially for female students. This study also describes a cyber tutoring program in which highly gifted girls between 13 and 16 years collaborate with and are supervised by female role models in higher positions in STEM. The evaluation results gained in the first year of this case study are promising and suggest that the extension and further development of the program would be advantageous.

Existing multi-label learning algorithms mostly assume that all class labels in an obtained data set are completely accurate. This assumption is rarely valid. As the scale of the label space increases, owing to limited domain knowledge and differences among methods of data acquisition, some examples will be unlabeled or mislabeled. The introduction of such noise results in the poor performance of trained multi-label classifiers. This paper proposes a new method for multi-label learning with label noise, called low-rank-constraint-based multi-label learning with label noise (ML-LRC), by incorporating low-rank matrix recovery and features selection into the learning process. First, we assume that two strongly correlated labels share more samples than two weakly correlated labels. Based on this assumption, the ML-LRC applies low-rank constraints to the label matrix to mine the local correlations of class labels and remove noise from the observed label matrix. Second, we believe that each class label only correlates to some features. So we control the sparsity of the coefficient matrix to filter out label-specific features, which can reduce the complexity of the model and greatly improve the performance of the classifier. The accelerated proximal gradient method and alternating direction method of multipliers are adopted to solve the problem. Experiments conducted on four benchmark multi-label datasets demonstrate the competitive performance of our proposed method compared with several state-of-the-art methods.

Existing multi-label learning classification algorithms ignore that class labels may be determined by some features in the original feature space. And only a partial label of each instance can be obtained for some real applications. Therefore, we propose a novel algorithm named joint Label-Specific features and Label Correlation for multi-label learning with Missing Label (LSLC-ML) and its optimized version to solve the above-mentioned problems. First, a missing label can be recovered by the learned positive and negative label correlations from the incomplete training data sets, then the label-specific features can be selected, finally the multi-label classification task can be modeled by combining the labelspecific feature selections, missing labels and positive and negative label correlations. The experimental results show that our algorithm LSLC-ML has strong competitiveness compared with some state-of-the-art algorithms in evaluation matrices when tested on benchmark multi-label data sets.

Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multilocation proteins to multiple proteins with single location, which does not take correlations among different subcellular locations into account. In this paper, a novel method named random label selection (RALS) (multilabel learning via RALS), which extends the simple binary relevance (BR) method, is proposed to learn from multilocation proteins in an effective and efficient way. RALS does not explicitly find the correlations among labels, but rather implicitly attempts to learn the label correlations from data by augmenting original feature space with randomly selected labels as its additional input features. Through the fivefold cross-validation test on a benchmark data set, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark data sets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multilocations of proteins. The prediction web server is available at http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

In multi-label image classification, each image is always associated with multiple labels and labels are usually correlated with each other. The intrinsic relation among labels can definitely contribute to classifier training. However, most previous studies on active learning for multi-label image classification purely mine label correlation based on observed label distribution. They ignore the mapping relation between examples and their labels. This mapping relation also implicates label relationship. Ignoring the mapping relation leads to an uncomprehensive label correlation estimation and results in a bad performance for classification. In this paper, we propose a novel multi-label active learning with low-rank mapping for image classification, called LMMAL, to solve this issue. More precisely, we train a low-rank mapping matrix to signify the mapping relation between the feature space and the label space of a certain multi-label dataset. Using this low-rank mapping relation, we exploit a full label correlation. Subsequently, an effective sampling strategy is designed by integrating this potential information with uncertainty to select the most informative example-label pairs. In addition, we extend LMMAL with automatic labeling (denoted as AL-LMMAL) to further reduce the annotation workload of active learning. Empirical results demonstrate the effectiveness of our approaches.

In multi-label learning, label-specific feature learning can effectively avoid some ineffectual features that interfere with the classification performance of the model. However, most of the existing label-specific feature learning algorithms improve the performance of the model for classification by constraining the solution space through label correlation. The non-equilibrium of the label distribution not only leads to some spurious correlations mixed in with the calculated label correlations but also diminishes the performance of the classification model. Causal learning can improve the classification performance and robustness of the model by capturing real causal relationships from limited data. Based on this, this paper proposes a causality-driven intra-class non-equilibrium label-specific features learning, named CNSF. Firstly, the causal relationship between the labels is learned by the Peter-Clark algorithm. Secondly, the label density of all instances is calculated by the intra-class non-equilibrium method, which is used to relieve the non-equilibrium distribution of original labels. Then, the correlation of the density matrix is calculated using cosine similarity and combined with causality to construct the causal density correlation matrix, to solve the problem of spurious correlation mixed in the label correlation obtained by traditional methods. Finally, the causal density correlation matrix is used to induce label-specific feature learning. Compared with eight state-of-the-art multi-label algorithms on thirteen datasets, the experimental results prove the reasonability and effectiveness of the algorithms in this paper.

Multi-label weak-label learning via semantic reconstruction and label correlations