benchmark datasets
Evaluating Entity Linking: An Analysis of Current Benchmark Datasets and a Roadmap for Doing a Better Job
... Many research papers treat benchmarks as black boxes, making it difficult to interpret efficacy improvements in terms of the individual contributions of algorithms and data. For system evaluations to provide ...
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Neural Network Optimization by Swarm Intelligence for Prediction & Classification of Benchmark Datasets
... ABSTRACT: Particle swarm optimization (PSO) is a computational method which optimizes a problem by having a population of candidate solutions, and moving these particles around in the search - space according to simple ...
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Performance Analysis of various classifiers using Benchmark Datasets in Weka tools
... redundant and irrelevant data that cause problem in network traffic classification. These kinds of data slow down the network and create difficulties in detecting cyber attacks. Intrusion detection system monitors the ...
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Adaptive linear combination of heuristic orderings in constructing examination timetables
... ITC2007 benchmark datasets are tested with only certain parameter settings, unlike the Toronto benchmark datasets, and with time limitation, these datasets might not show the exact ...
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Minimum density hyperplanes in the feature space
... Abstract—We introduce a kernel formulation of the recently proposed minimum density hyperplane approach to clustering. This enables the identification of clusters that are not linearly separable in the input space by ...
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LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data
... SMOTE (Synthetic Minority Over-sampling Technique) [7] is a powerful over-sampling method that has shown a great deal of success in class imbalanced problems. The SMOTE algorithm calculates a distance of the feature ...
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A Survey in Deep Learning Model for Image Annotation
... Some benchmark datasets including Flickr8K, Flickr30K and MSCOCO were used to produce the results by applying both human evaluation and automatic evaluation ...
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Mathematical Morphology based Retinal Image Blood Vessels Segmentation
... two benchmark datasets ...the benchmark datasets such as DRIVE and ...image datasets in Fig.2 and CHASE_DB1 retinal image datasets in ...the datasets presented ...
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Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study
... Abstract Feature selection plays a key role in multi- voxel pattern analysis because functional magnetic reso- nance imaging data are typically noisy, sparse, and high- dimensional. Although the conventional evaluation ...
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Automatic extraction of protein-protein interactions using grammatical relationship graph
... Bui et al. has developed a hybrid approach for extract- ing PPIs [78]. The method consists of two phases. First, the data were automatically categorized into subsets based on its semantic properties and candidate PPI ...
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Computer Model for Evaluating Multi Target Tracking Algorithms
... Public benchmark datasets have been widely used to evaluate multi-target tracking ...the benchmark datasets should include the video scenes of all scenarios that need to be ...available ...
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A Challenge Dataset and Effective Models for Aspect Based Sentiment Analysis
... ABSA datasets have been con- structed, including SemEval-2014 Restaurant Re- view dataset, Laptop Review dataset (Pontiki et ...three datasets have since become the benchmark datasets for the ...
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MRMR BA: A HYBRID GENE SELECTION ALGORITHM FOR CANCER CLASSIFICATION
... microarray benchmark datasets: Colon, Breast, and ...all benchmark datasets, the MRMR-BA achieved better performance in terms of highest classification accuracy along with the lowest average ...
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CRUISE: Cold Start New Skill Development via Iterative Utterance Generation
... We further evaluate the CRUISE dataset subjectively by soliciting judgments from Amazon Mechanical Turkers. Each turker was presented a task of rating utterances sampled from mixed CRUISE and human generated ...
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A FRAMEWORK FOR ARABIC SENTIMENT ANALYSIS USING MACHINE LEARNING CLASSIFIERS
... In recent years, the use of Internet and online comments, expressed in natural language text, have increased significantly. However, it is difficult for humans to read all these comments and classify them appropriately. ...
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Feature Selection With Multi Objective Genetic Algorithm And Fuzzy Rule-Based Multiclassifiers For Cancer Classification
... six benchmark gene microarray datasets (including both binary and multi-class classification problems), we demonstrate experimentally that our proposed scheme can achieve significant empirical success and ...
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BERT based Lexical Substitution
... To address the above issues, we propose a novel BERT-based lexical substitution approach, moti- vated by that BERT (Devlin et al., 2018) not only can predict the distribution of a masked target word conditioned on its ...
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Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling
... On datasets 1 and 13, the improvements were ...on datasets 2, 3, 5, 7, 8, 9, 10, and ...on datasets 4, 8, 10, and ...our datasets, al- though using multiple layers leads to better predic- ...
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Text Classification on Dataset of Marine and Fisheries Sciences Domain using Random Forest Classifier
... The datasets used in this research are the ChaLearn dataset contained 7754 gesture instances (from 20 Italian communication gestures) and the NATOPS dataset ...
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Hawkes processes for continuous time sequence classification : an application to rumour stance classification in Twitter
... ter datasets and experimenting on rumour stance classification of tweets, we have shown that HP is a competitive approach, which outperforms a range of strong benchmark methods by providing the multinomial ...
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