Learning To Rank Research Articles

Ranking based ontology learning algorithm for similarity measuring and ontology mapping using representation theory

Ontology, as a semantic analysis and computation framework, has been widely used in various ways. Recently, learning algorithms have been employed for ontology similarity measuring and ontology mapping which aims to get a score ontology function and thus map each vertex on ontology graph to a real number. In this paper, ranking learning approach is employed in ontology function learning. The framework is designed based on reproducing kernel Hilbert space technology and representation theory trick. Finally, four experiments are manifested for the application of ontology algorithms in different fields. The effectiveness is proved by the comparison between data results.

Learning to Branch in Mixed Integer Programming

The design of strategies for branching in Mixed Integer Programming (MIP) is guided by cycles of parameter tuning and offline experimentation on an extremely heterogeneous testbed, using the average performance. Once devised, these strategies (and their parameter settings) are essentially input-agnostic. To address these issues, we propose a machine learning (ML) framework for variable branching in MIP.Our method observes the decisions made by Strong Branching (SB), a time-consuming strategy that produces small search trees, collecting features that characterize the candidate branching variables at each node of the tree. Based on the collected data, we learn an easy-to-evaluate surrogate function that mimics the SB strategy, by means of solving a learning-to-rank problem, common in ML. The learned ranking function is then used for branching. The learning is instance-specific, and is performed on-the-fly while executing a branch-and-bound search to solve the MIP instance. Experiments on benchmark instances indicate that our method produces significantly smaller search trees than existing heuristics, and is competitive with a state-of-the-art commercial solver.

Complete lattice learning for multivariate mathematical morphology

The generalization of mathematical morphology to multivariate vector spaces is addressed in this paper. The proposed approach is fully unsupervised and consists in learning a complete lattice from an image as a nonlinear bijective mapping, interpreted in the form of a learned rank transformation together with an ordering of vectors. This unsupervised ordering of vectors relies on three steps: dictionary learning, manifold learning and out of sample extension. In addition to providing an efficient way to construct a vectorial ordering, the proposed approach can become a supervised ordering by the integration of pairwise constraints. The performance of the approach is illustrated with color image processing examples.

Scaling-up empirical risk minimization

In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by U-statistics of degree d ≥ 1, i.e. functionals of t...

Cloud estimation of distribution algorithm with quasi-oppositional learning and preference order ranking for multi-objective optimisation

Cloud estimation of distribution algorithm with quasi-oppositional learning and preference order ranking for multi-objective optimisation

User-adaptive image retrieval via fusing pointwise and pairwise labels

In many Web image retrieval applications, adapting the retrieval results according to some model of the user is a desired feature as the returned images can be made specifically relevant to a user’s needs. Making retrieval user-adaptive faces several practical challenges, including the ambiguity of user query, the lack of user-adaptive training data, and lack of proper mechanisms for supporting adaptive learning. To address some of these challenges, we propose a hybrid learning strategy that fuses knowledge from both pointwise and pairwise training data into one framework for attribute-based, user-adaptive image retrieval. An online learning algorithm is developed for updating the ranking performance based on user feedback. The framework is also derived into a kernel form allowing easy application of kernel techniques. We use both synthetic and real-world datasets to evaluate the performance of the proposed algorithm. Comparison with other state-of-the-art approaches suggests that our method achieves obvious performance gains over ranking and zero-shot learning. Further, our online learning algorithm was found to be able to deliver much better performance than batch learning, given the same elapsed running time.

Ranking Models and Learning to Rank: A Survey

Ranking Models and Learning to Rank: A Survey

Classification and saliency detection by semi-supervised low-rank representation

In the area of pattern recognition, Low Rank Representation (LRR) is an efficient method in recovering the subspace structure of the dataset. However, LRR is unsupervised. Without any label information, LRR constructs an informative graph which is then combined with the mature graph-based semi-supervised learning (GSSL) framework to complete the classification task. In this paper, we propose a new low rank learning method which constructs the low rank representation matrix utilizing label information to obtain a more informative graph. This method integrates the low rank graph construction and the label information propagation processes together. Thus the optimization of the low rank representation and the soft label prediction function are calculated iteratively at the same time. We name this method as Semi-Supervised Low Rank Learning (SSLRL). It enhanced the classification performance of traditional LRR-Graph based SSL by 5–30% and the running time is reduced from hundreds to less than ten seconds. Based on this method, a new outlier detection strategy is presented. This strategy succeeds with an AUC of at least 93% even if the detection condition of LRR is not satisfied. The effectiveness of SSLRL is demonstrated in semi-supervised classification, outlier detection, and salient detection tasks. These extensive experimental results highlight the outperforming of our method over state-of-the-art methods.

Towards Automated Risk Assessment and Mitigation of Mobile Applications

Mobile operating systems, such as Apple’s iOS and Google’s Android, have supported a ballooning market of feature-rich mobile applications. However, helping users understand and mitigate security risks of mobile applications is still an ongoing challenge. While recent work has developed various techniques to reveal suspicious behaviors of mobile applications, there exists little work to answer the following question: are those behaviors necessarily inappropriate ? In this paper, we seek an approach to cope with such a challenge and present a continuous and automated risk assessment framework called RiskMon that uses machine-learned ranking to assess risks incurred by users’ mobile applications, especially Android applications. RiskMon combines users’ coarse expectations and runtime behaviors of trusted applications to generate a risk assessment baseline that captures appropriate behaviors of applications. With the baseline, RiskMon assigns a risk score on every access attempt on sensitive information and ranks applications by their cumulative risk scores. Furthermore, we demonstrate how RiskMon supports risk mitigation with automated permission revocation. We also discuss a proof-of-concept implementation of RiskMon as an extension of the Android mobile platform and provide both system evaluation and usability study of our methodology.

Concept selection for phenotypes and diseases using learn to rank.

BackgroundPhenotypes form the basis for determining the existence of a disease against the given evidence. Much of this evidence though remains locked away in text – scientific articles, clinical trial reports and electronic patient records (EPR) – where authors use the full expressivity of human language to report their observations.ResultsIn this paper we exploit a combination of off-the-shelf tools for extracting a machine understandable representation of phenotypes and other related concepts that concern the diagnosis and treatment of diseases. These are tested against a gold standard EPR collection that has been annotated with Unified Medical Language System (UMLS) concept identifiers: the ShARE/CLEF 2013 corpus for disorder detection. We evaluate four pipelines as stand-alone systems and then attempt to optimise semantic-type based performance using several learn-to-rank (LTR) approaches – three pairwise and one listwise. We observed that whilst overall Apache cTAKES tended to outperform other stand-alone systems on a strong recall (R = 0.57), precision was low (P = 0.09) leading to low-to-moderate F1 measure (F1 = 0.16). Moreover, there is substantial variation in system performance across semantic types for disorders. For example, the concept Findings (T033) seemed to be very challenging for all systems. Combining systems within LTR improved F1 substantially (F1 = 0.24) particularly for Disease or syndrome (T047) and Anatomical abnormality (T190). Whilst recall is improved markedly, precision remains a challenge (P = 0.15, R = 0.59).

Active Learning for Ranking through Expected Loss Optimization

Learning to rank arises in many data mining applications, ranging from web search engine, online advertising to recommendation system. In learning to rank, the performance of a ranking model is strongly affected by the number of labeled examples in the training set; on the other hand, obtaining labeled examples for training data is very expensive and time-consuming. This presents a great need for the active learning approaches to select most informative examples for ranking learning; however, in the literature there is still very limited work to address active learning for ranking. In this paper, we propose a general active learning framework, expected loss optimization (ELO), for ranking. The ELO framework is applicable to a wide range of ranking functions. Under this framework, we derive a novel algorithm, expected discounted cumulative gain (DCG) loss optimization (ELO-DCG), to select most informative examples. Then, we investigate both query and document level active learning for raking and propose a two-stage ELO-DCG algorithm which incorporate both query and document selection into active learning. Furthermore, we show that it is flexible for the algorithm to deal with the skewed grade distribution problem with the modification of the loss function. Extensive experiments on real-world web search data sets have demonstrated great potential and effectiveness of the proposed framework and algorithms.

Active learning for ranking with sample density

While ranking is widely used in many online domains such as search engines and recommendation systems, it is non-trivial to label enough data examples to build a high performance machine-learned ranking model. To relieve this problem, active learning has been proposed, which selectively labels the most informative examples. However, data density, which has been proven helpful for data sampling in general, is ignored by most of the existing active learning for ranking studies. In this paper, we propose a novel active learning for ranking framework, generalization error minimization (GEM), which incorporates data density in minimizing generalization error. Concentrating on active learning for search ranking, we employ classical kernel density estimation to infer data density. Considering the unique query---document structure in ranking data, we estimate sample density at both query level and document level. Under the GEM framework, we propose new active learning algorithms at both query level and document level. Experimental results on the LETOR 4.0 data set and a real-world Web search ranking data set from a commercial search engine have demonstrated the effectiveness of the proposed active learning algorithms.

Active Learning for Web Search Ranking via Noise Injection

Learning to rank has become increasingly important for many information retrieval applications. To reduce the labeling cost at training data preparation, many active sampling algorithms have been proposed. In this article, we propose a novel active learning-for-ranking strategy called ranking-based sensitivity sampling (RSS), which is tailored for Gradient Boosting Decision Tree (GBDT), a machine-learned ranking method widely used in practice by major commercial search engines for ranking. We leverage the property of GBDT that samples close to the decision boundary tend to be sensitive to perturbations and design the active learning strategy accordingly. We further theoretically analyze the proposed strategy by exploring the connection between the sensitivity used for sample selection and model regularization to provide a potentially theoretical guarantee w.r.t. the generalization capability. Considering that the performance metrics of ranking overweight the top-ranked items, item rank is incorporated into the selection function. In addition, we generalize the proposed technique to several other base learners to show its potential applicability in a wide variety of applications. Substantial experimental results on both the benchmark dataset and a real-world dataset have demonstrated that our proposed active learning strategy is highly effective in selecting the most informative examples.

Ranking key nodes in complex networks by considering structural holes

Structural hole nodes in complex networks play important roles in the network information diffusion. Unfortunately, most of the existing methods of ranking key nodes do not integrate structural hole nodes and other key nodes. According to the relevant research on structural hole theory as well as the key node ranking methods, network constraint coefficient, betweenness centrality, hierarchy, efficiently, network size, PageRank and clustering coefficient, 7 metrics are selected to rank the key nodes. Based on the 7 metrics, a ranking learning method based on ListNet is introduced to solve ranking key nodes by multi metrics. Comprehensive experiments are conducted based on different artificial networks and real complex networks. Experimental results with manual annotation show that the ranking method can comprehensively consider the structural hole nodes and other nodes with different important features. The ranking results on different networks are highly consistent with the manual ranking results. The spreading experiment results using signed to interference ratio propagation model show that SIR model can reach a maximum propagating ratio in a shorter propagating time initiated by TOP-K key nodes selected by our method than TOP-K key nodes selected by other methods.

Learning to rank diversified results for biomedical information retrieval from multiple features.

BackgroundDifferent from traditional information retrieval (IR), promoting diversity in IR takes consideration of relationship between documents in order to promote novelty and reduce redundancy thus to provide diversified results to satisfy various user intents. Diversity IR in biomedical domain is especially important as biologists sometimes want diversified results pertinent to their query.MethodsA combined learning-to-rank (LTR) framework is learned through a general ranking model (gLTR) and a diversity-biased model. The former is learned from general ranking features by a conventional learning-to-rank approach; the latter is constructed with diversity-indicating features added, which are extracted based on the retrieved passages' topics detected using Wikipedia and ranking order produced by the general learning-to-rank model; final ranking results are given by combination of both models.ResultsCompared with baselines BM25 and DirKL on 2006 and 2007 collections, the gLTR has 0.2292 (+16.23% and +44.1% improvement over BM25 and DirKL respectively) and 0.1873 (+15.78% and +39.0% improvement over BM25 and DirKL respectively) in terms of aspect level of mean average precision (Aspect MAP). The LTR method outperforms gLTR on 2006 and 2007 collections with 4.7% and 2.4% improvement in terms of Aspect MAP.ConclusionsThe learning-to-rank method is an efficient way for biomedical information retrieval and the diversity-biased features are beneficial for promoting diversity in ranking results.

Interactive Learning of Pattern Rankings

Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data mining expertise or effort from the data analyst, and hence cannot be used by typical domain experts. To address this, we introduce a generic framework for interactive learning of userspecific pattern ranking functions. The user is only asked to rank small sets of patterns, while a ranking function is inferred from this feedback by preference learning techniques. Moreover, we propose a number of active learning heuristics to minimize the effort required from the user, while ensuring that accurate rankings are obtained. We show how the learned ranking functions can be used to mine new, more interesting patterns. We demonstrate two concrete instances of our framework for two different pattern mining tasks, frequent itemset mining and subgroup discovery. We empirically evaluate the capacity of the algorithm to learn pattern rankings by emulating users. Experiments demonstrate that the system is able to learn accurate rankings, and that the active learning heuristics help reduce the required user effort. Furthermore, using the learned ranking functions as search heuristics allows discovering patterns of higher quality than those in the initial set. This shows that machine learning techniques in general, and active preference learning in particular, are promising building blocks for interactive data mining systems.

Sparse ranking model adaptation for cross domain learning to rank

Cross domain learning to rank problem has become a hot topic issue in transfer learning and learning to rank communities. In this problem, there are scarce humanlabeled data in the target domain, but sufficient labeled data exist in a related domain named as source domain. In order to obtain an effective ranking model for target domain, various ranking adaptation frameworks are proposed to learn ranking models with the help of the labeled data in the source domain. In this paper, wepropose a sparse model adaptation framework, which utilizes l1 regularization to transfer the most confident prior knowledge from source domain to the target domain. Due to the sparsity-inducing property of the l1 regularization, the framework is able to reduce the negative effects of the feature gap between source domain data and target domain data. However, the optimization problem formulated by the framework is non-differentiable. It is difficult to obtain the solution by the most popular methods. To address this problem, we design an efficient algorithm from the primal-dual perspective. Empirical evaluation over LETOR benchmark data [26] collections validates that the proposed algorithm can significantly improve the accuracy of the ranking prediction.

A novel technique to extract accurate cell contours applied for segmentation of phytoplankton images

Active contour model (ACM) is an image segmentation technique widely applied for object detection. Most of the research in ACM area is dedicated to the development of various energy functions based on physical intuition. Here, instead of constructing a new energy function, we manipulate values of ACM parameters to generate a multitude of potential contours, score them using a machine-learned ranking technique, and select the best contour for each object in question. Several learning-to-rank (L2R) methods are evaluated with a goal to choose the most accurate in assessing the quality of generated contours. Superiority of the proposed segmentation approach over the original boosted edge-based ACM and three ACM implementations using the level-set framework is demonstrated for the task of Prorocentrum minimum cells' detection in phytoplankton images. Experiments show that diverse set of contour features with grading learned by a variant of multiple additive regression trees ($$\uplambda $$?-MART) helped to extract precise contour for 87.6 % of cells tested.

Ordinal Distance Metric Learning for Image Ranking

Recently, distance metric learning (DML) has attracted much attention in image retrieval, but most previous methods only work for image classification and clustering tasks. In this brief, we focus on designing ordinal DML algorithms for image ranking tasks, by which the rank levels among the images can be well measured. We first present a linear ordinal Mahalanobis DML model that tries to preserve both the local geometry information and the ordinal relationship of the data. Then, we develop a nonlinear DML method by kernelizing the above model, considering of real-world image data with nonlinear structures. To further improve the ranking performance, we finally derive a multiple kernel DML approach inspired by the idea of multiple-kernel learning that performs different kernel operators on different kinds of image features. Extensive experiments on four benchmarks demonstrate the power of the proposed algorithms against some related state-of-the-art methods.

Sparse structure regularized ranking

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it ...