Ranking Algorithm Research Articles

Topo-heuristic reconfiguration of algebraic LSTM components to optimize temporal modulation in long-range dependencies

In this paper, the authors present a modified LambdaRank ranking algorithm based on the mathematical apparatus of the basic machine learning model (LSTM). LambdaRank is an effective method for ranking objects according to their importance, so it is often used as a mandatory component in search engines and recommendation systems. In this paper, an improvement of the algorithm is proposed by using optimisation techniques and introducing additional parameters for more accurate and stable ranking. The effectiveness of the proposed approach is verified on experimental real application data. The obtained accuracy results of the upgraded algorithm have also been analysed and compared with the classical variation of LambdaRank.

Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile; this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that; the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

Application of Text Rank Algorithm Fused With LDA in Information Extraction Model

Application of Text Rank Algorithm Fused With LDA in Information Extraction Model

Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.

Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature selection method was applied to each list for obtaining the optimal feature subsets and building high-performance classification models. Furthermore, a series of classification rules were accessed by decision tree with the highest performance. Eventually, the rationality of the mined isoforms (features) and classification rules was verified by reviewing previous research. Features such as isoforms ENST00000464835 (expressed by LRRN3), ENST00000622663 (expressed by SASH1), and ENST00000284311 (expressed by GPR15), and pathways (cytotoxicity mediated by natural killer cell and cytokine-cytokine receptor interaction) revealed by the enrichment analysis, were highly relevant to smoking response, suggesting the robustness of our analysis pipeline.

A Novel Architecture for Search Engine using Domain Based Web Log Data

Search engines, an information retrieval tool are the main source of information for users’ information need now a day. For every query, the search engine explores its repository and/or indexer to find the relevant documents/URLs for that query. Page ranking algorithms rank the Uniform Resource Locator in abstract section (URLs) according to its relevancy with respect to users’ query. It is analyzed that many of the queries fired by users on search engines are duplicate. There is a scope to improve the performance of search engine to reduce its efforts for duplicate queries. In this paper a proxy server is created that keep store the search results of user queries in web log. The proposed proxy server uses this web log to find results faster for duplicate queries fired next time. The proposed scheme has been tested and found prominent. The proposed architecture tested for ten duplicate user queries. it return all relevant web pages for duplicate user query (if query is found in web log at proxy server) from a particular domain instead of entire database. It reduces the perceived latency for duplicate query and also improves the value of precession and accuracy up to 81.8% and 99% respectively for all duplicate user queries.

Distributed spectral pairwise ranking algorithms

This paper considers spectral pairwise ranking algorithms in a reproducing kernel Hilbert space. The concerned algorithms include a large family of regularized pairwise learning algorithms. Motivated by regularization methods, spectral algorithms are proposed to solve ill-posed linear inverse problems, then developed in learning theory and inverse problems. Recently, pairwise learning tasks such as bipartite ranking, similarity metric learning, Minimum Error Entropy principle, and AUC maximization have received increasing attention due to their wide applications. However, the spectral algorithm acts on the spectrum of the empirical integral operator or kernel matrix, involving the singular value decomposition or the inverse of the matrix, which is time-consuming when the sample size is immense. Our contribution is twofold. First, under some general source conditions and capacity assumptions, we establish the first-ever mini-max optimal convergence rates for spectral pairwise ranking algorithms. Second, we consider the distributed version of the algorithms based on a divide-and-conquer approach and show that, as long as the partition of the data set is not too large, the distributed learning algorithm enjoys both computational efficiency and statistical optimality.

FIFA ranking: Evaluation and path forward

In this work, we study the ranking algorithm used by Fédération Internationale de Football Association (FIFA); we analyze the parameters that it currently uses, show the formal probabilistic model from which it can be derived, and optimize the latter. In particular, analyzing games since the introduction of the algorithm in 2018, we conclude that game’s “importance” (defined by FIFA and used by the algorithm) is counterproductive from the point of view of the predictive capacity of the algorithm. We also postulate that the algorithm should be rooted in the formal modeling principle, where the Davidson model proposed in 1970 seems to be an excellent candidate, preserving the form of the algorithm currently used. The results indicate that the predictive capacity of the algorithm is considerably improved by using the home-field advantage (HFA), as well as the explicit model for the draws in the game. Moderate but notable improvement may be achieved by introducing the weighting of the results with the goal differential, which, although not rooted in a formal modeling principle, is compatible with the current algorithm and can be tuned to the characteristics of the football competition.

Machine learning and ontology-based novel semantic document indexing for information retrieval

The goal of information retrieval (IR) systems is to find the contents most closely related to the user's information needs from a pool of information. However, conventional IR methods neglect semantic descriptions of document contents and index documents based on the words that they include. When users and indexing systems use different terms to express the same subject, a vocabulary gap emerges. To overcome this limitation and to enhance the effectiveness of the IR systems, this paper introduced a novel hybrid semantic document indexing employing machine learning and domain ontology. The presented technique uses a skip-gram with negative sampling-based machine learning model and a domain ontology to determine the concepts for annotating unstructured documents. The proposed work also introduced multiple feature based novel concept ranking algorithm where statistical, semantic, and scientific named entity features of the concept were used to assign relevance weight to the annotations. The fuzzy analytical hierarchy process was used to derive the parameters of these feature weights. The final step is to rank the concepts according to their relevance to the document. Five benchmark publicly accessible datasets from the computer science domain were used in a series of experiments to validate the results of presented method. Experiment findings showed that the proposed method performs better than state-of-the-art techniques on these datasets, by improving average accuracy by 29%, while an improvement of 25% was recorded in F-measure. The improvement in average accuracy demonstrates that the performance of the proposed approach is better than the state-of-the-art methods in extracting document concepts accurately even when the same concept is referred to by distinct terms in the document and domain ontologies. The proposed system's ability to find similar concepts when the documents possess no concept from domain ontology is demonstrated by the improvement in F-measure, which is attributed to high recall rates of the proposed indexing scheme while maintaining high accuracy.

Spammer detection via ranking aggregation of group behavior

Online review networks have become a popular platform for disseminating information which attracts large numbers of spammers to influence the reputation of the online products by writing fake reviews. Thus, the spammer detection becomes crucial in controlling the fraud reviews which is valuable for practical applications. However, existing ranking-based methods usually focus on the rating value and textual features of reviews ignoring the features of group behavior which results in the sharp degradation of spammer detection’s performance in case of large-scale spammer group attack. In this paper, we propose a novel ranking aggregation method based on features of collusive attack of spammer groups to optimize the spammer ranking algorithm by reassigning the spamicity score for each reviewer. Extensive experiments on various real-world datasets with spammer injection demonstrate that our proposed optimization method can significantly improve the accuracy and robustness of the spammer detection. The average improvements of performance for ranking before and after optimization achieve 10.73%, 42.86% and 4.32% respectively on the Recall, Precision and AUC metrics.

Computer-aided diagnosis system for cardiac disorders using variational mode decomposition and novel cepstral quinary patterns

Cardiac disorders cause a large number of human mortalities every year. This raises a sheer need for an early and accurate diagnosis of cardiac disorders to provide early meaningful intervention. Ischemic heart disease (IHD) and rheumatic heart disease (RHD) are the leading causes of heart failure. In this article, we proposed a novel framework for the classification of IHD and RHD using Pulse Plethysmograph (PuPG) signals obtained from a subject’s fingertip. The presented framework comprises a combination of variational mode decomposition (VMD), cosine-based soft segmentation, and novel cepstral quinary patterns (CQPs). The PuPG signals were first preprocessed through VMD by decomposing them in various modes. After an extensive time–frequency analysis, only relevant modes were selected and combined to reconstruct a preprocessed PuPG signal. The preprocessed signals were segmented through developed cosine-based soft segmentation to eliminate similar content in various classes. Features were extracted from the preprocessed signal using novel CQPs. CQPs were able to extract the hidden discriminative information about the disease through cepstrum transformed representation. The extracted CQP features were further reduced through the ReliefF ranking algorithm. The extracted reduced features were exposed to a range of well-known classification methods such as Support Vector Machines (SVM) with linear and non-linear kernels, Ensemble classifiers, and K-nearest neighbors. SVM-Gaussian (SVMG) provides the best performance of 99% accuracy using 10-fold cross-validation. The proposed CQPs were also compared with time, frequency, and cepstral features. Comparative analysis confirms that the proposed method outperforms the existing renowned techniques for the diagnosis of cardiac disorders.

LncRNA-disease association identification using graph auto-encoder and learning to rank.

Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload of wet laboratory experiments. Therefore, it is greatly important to develop an efficient computational method for predicting potential lncRNA-disease associations. Previous methods showed that combining the prediction results of the lncRNA-disease associations predicted by different classification methods via Learning to Rank (LTR) algorithm can be effective for predicting potential lncRNA-disease associations. However, when the classification results are incorrect, the ranking results will inevitably be affected. We propose the GraLTR-LDA predictor based on biological knowledge graphs and ranking framework for predicting potential lncRNA-disease associations. Firstly, homogeneous graph and heterogeneous graph are constructed by integrating multi-source biological information. Then, GraLTR-LDA integrates graph auto-encoder and attention mechanism to extract embedded features from the constructed graphs. Finally, GraLTR-LDA incorporates the embedded features into the LTR via feature crossing statistical strategies to predict priority order of diseases associated with query lncRNAs. Experimental results demonstrate that GraLTR-LDA outperforms the other state-of-the-art predictors and can effectively detect potential lncRNA-disease associations. Availability and implementation: Datasets and source codes are available at http://bliulab.net/GraLTR-LDA.

A Novel Method for Simulating Micro-Scale Water Droplet Movements

Micro-scale fluids are tiny droplets that adhere to the surface of an object as a result of rainfall, perspiration, etc. Micro-scale fluid simulation is widely used in fields such as film and games. The existing state-of-the-art simulation methods are not suitable for simulating water droplets moving on a surface due to the fact that the water droplets cannot leave the texture space and their movements always depend on the continuous UV region. In this study, a novel method for simulating water droplets moving on a surface is proposed. We divide the droplets into two types: (1) two-dimensional droplets and (2) three-dimensional droplets and we implement the transformation between two-dimensional droplets in the texture space and three-dimensional droplets in the physical space. In the preprocessing phase, jump textures, coordinate transform textures and force field textures are generated in the non-continuous UV regions on a 3D object’s surface. In the process of simulation, water droplets are treated as rigid particles. The Velocity-Verlet-based method is adopted to solve the motion trajectory equation, and the boundary droplet transport algorithm is implemented based on jump texture. In the process of rendering, the height map is generated according to the simulation in the texture space and then the liquid bridge phenomenon between the droplets is simulated based on the Gaussian blur and the color rank algorithm. Finally, they are converted into normal texture-rendering droplets. The experimental result shows that the proposed method works well when simulating the movements of water droplets on a surface in a real-time manner, and it makes the movement simulation of dimension-reducing water droplets no longer depend on the continuous surface and continuous UV region. Moreover, the simulation efficiency of the proposed method is two times higher than that of the Smoothed Particle Hydrodynamics (SPH) method.

Dynamic reaction picklist for improving allergy reaction documentation: A usability study

ObjectiveTo assess novel dynamic reaction picklists for improving allergy reaction documentation compared to a static reaction picklist. Materials and methodsWe developed three web-based user interfaces (UIs) mimicking the Mass General Brigham’s EHR allergy module: the first and second UIs (i.e., UI-1D, UI-2D) implemented two dynamic reaction picklists with different ranking algorithms and the third UI (UI-3S) implemented a static reaction picklist like the one used in the current EHR. We recruited 18 clinicians to perform allergy entry for 10 test cases each via UI-1D and UI-3S, and another 18 clinicians via UI-2D and UI-3S. Primary measures were the number of free-text entries and time to complete the allergy entry. Clinicians were also interviewed using 30 questions before and after the data entry. Results and discussionsAmong 36 clinicians, less than half were satisfied with the current EHR reaction picklists, due to their incomprehensiveness, inefficiency, and lack of intuitiveness. The clinicians used significantly fewer free-text entries when using UI-1D or UI-2D compared to UI-3S (p < 0.05). The clinicians used on average 51 s (15 %) less time via UI-1D and 50 s (16 %) less time via UI-2D in completing the allergy entries versus UI-3S, and there was not a statistically significant difference in documentation time for either group between the dynamic and static UIs. Overall, 15–17 (83–94 %) clinicians rated UI-1D and 13–15 (72–83 %) clinicians rated UI-2D as efficient, easy to use, and useful, while less than half rated the same for UI-3S. Most clinicians reported that the dynamic reaction picklists always or often suggested appropriate reactions (n = 30, 83 %) and would decrease the free-text entries (n = 26, 72 %); nearly all preferred the dynamic picklist over the static picklist (n = 32, 89 %). ConclusionWe found that dynamic reaction picklists significantly reduced the number of free-text entries and could reduce the time for allergy documentation by 15%. Clinicians preferred the dynamic reaction picklist over the static picklist.

Prioritization of watersheds based on morphometric assessment in relation to flood management: A case study of Ajay river basin, Eastern India

Morphometric analysis and sub-watershed prioritising were carried out to ascertain the Ajay river basin's susceptibility to flooding. The whole Ajay river basin was divided into seven smaller watersheds, and a ranking algorithm was used to the morphometric features of these smaller watersheds to determine their prioritization rank. 13 distinct morphometric criteria of linear, relief and areal charactershave been found and used to rank and priorities sub-watersheds, for critical analysis. Some morphometric parameters have a direct correlation with flood occurrence, whilst others have an indirect association with flood occurrence. According to their relative significance and impact on the floods, all parameters have been given weighted ratings. The basin with the lowest parameter value is placed first in the ranking. According to the results of the study, sub-watershed 7, which is located in the lower catchment of the Ajay river basin, is very vulnerable to floods. The findings of this research may be utilized as advice by competent authorities in the early stages of flood mitigation efforts, if necessary.

Retraction Note: A novel approach for ranking customer reviews using a modified PSO-based aspect ranking algorithm

Retraction Note: A novel approach for ranking customer reviews using a modified PSO-based aspect ranking algorithm

Page Rank Algorithm in Hadoop By MapReduce Framework

One of the most popular algorithms in processing internet data i.e. web pages is a site ranking algorithm that is intended to decide the importance of web pages by attribution weight value based on any incoming link to this site.Large amounts of internet data can lead to computational load in processing the page ranking algorithm.To take this burden into account, in this article we present a an algorithm for processing site rankings through a distributed system A Hadoop MapReduce framework called MR PageRank. This paper intended for the first analysis of input raw web pages create the page name and its outbound links as key and value of the pair, respectively, as well as the total weight of the hanging nodes and total number of pages. Next, we calculate the probability each page and divide this probability by each outgoing link evenly. Each outgoing weight is mixed and aggregated based on page similarity to new update weight value of each page. Keywords: pagerank, hadoop, mapreduce, Attribution weight...

Fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms

fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms

GAMB-GNN: Graph Neural Networks learning from gene structure relations and Markov Blanket ranking for cancer classification in microarray data

Microarray data plays a significant role in the classification and prediction of cancer, which has become an essential area of research in computational biology and bioinformatics. Several machine learning methods have been applied to help classify and predict cancer using microarray data. However, previous methods have mainly leveraged the expression level of a subset of genes as a sample feature. These methods lack the utilization of the structure relation information among genes. At the same time, there is a lack of analysis of redundancy among features in the existing gene selection algorithms. This paper proposes a Graph Neural Networks model with the Markov Blanket ranking method for cancer classification (GAMB-GNN) in microarray data to combat these challenges. The problem of cancer classification in the microarray data is considered by gene attributes and multi-type relation networks among genes in our model. The proposed GAMB-GNN obtains the gene scores and ranking according to the relevance of genes and cancer by the gene ranking algorithm based on Markov Blanket and then selects a top fraction of the ranking list to construct the multi-type gene relations graph. Further, GAMB-GNN employs a graph classifier with a relation attention mechanism and node aggregation pooling to learn the multiple relations structure and weight to obtain a better feature representation of the sample. The proposed model is validated through experiments in six public microarray datasets. The experiment results show that the accuracy and f1-score of our method on the cancer classification task on all tested microarray datasets improved by 3.98%–24.36% and 4.22%–31.93% on average over the baseline and state-of-the-art methods.

RelRank: A relevance-based author ranking algorithm for individual publication venues

Hiring appropriate editors, chairs and committee members for academic journals and conferences is challenging. It requires a targeted search for high profile scholars who are active in the field as well as in the publication venue. Many author-level metrics have been employed for this task, such as the h-index, PageRank and their variants. However, these metrics are global measures which evaluate authors’ productivity and impact without differentiating the publication venues. From the perspective of a venue, it is also important to have a localised metric which can specifically indicate the significance of academic authors for the particular venue. In this paper, we propose a relevance-based author ranking algorithm to measure the significance of authors to individual venues. Specifically, we develop a co-authorship network considering the author-venue relationship which integrates the statistical relevance of authors to individual venues. The RelRank, an improved PageRank algorithm embedding author relevance, is then proposed to rank authors for each venue. Extensive experiments are carried out to analyse the proposed RelRank in comparison with classic author-level metrics on three datasets of different research domains. We also evaluate the effectiveness of the RelRank and comparison metrics in recommending editorial boards of three venues using test data. Results demonstrate that the RelRank is able to identify not only the high profile scholars but also those who are particularly significant for individual venues.

Virtual Sensing Network for Statistical Process Monitoring

Physical sensing is increasingly implemented in modern industries to improve information visibility, which brings real-time signals that are spatially distributed and temporally varying. These signals are often nonlinear and nonstationary in the high-dimensional space, which pose significant challenges to monitoring and control of complex systems. Therefore, this paper presents a new “virtual sensing” approach that places imaginary sensors at different locations in signaling trajectories to monitor evolving dynamics within the signal space. First, we propose self-organizing principles to investigate distributional and topological features of nonlinear signals for optimal placement of imaginary sensors. Second, we design and develop the network model to represent real-time flux dynamics among these virtual sensors, in which each node represents a virtual sensor, while edges signify signal flux among sensors. Third, the establishment of a network model as well as the notion of transition uncertainty enable a fine-grained view into system dynamics and then extend a new Flux Rank (FR) algorithm for process monitoring. Experimental results show that the network FR methodology not only delineate real-time flux patterns in nonlinear signals, but also effectively monitor spatiotemporal changes in the dynamics of nonlinear dynamical systems.