18,505 research outputs found
Deep Architectures and Ensembles for Semantic Video Classification
This work addresses the problem of accurate semantic labelling of short
videos. To this end, a multitude of different deep nets, ranging from
traditional recurrent neural networks (LSTM, GRU), temporal agnostic networks
(FV,VLAD,BoW), fully connected neural networks mid-stage AV fusion and others.
Additionally, we also propose a residual architecture-based DNN for video
classification, with state-of-the art classification performance at
significantly reduced complexity. Furthermore, we propose four new approaches
to diversity-driven multi-net ensembling, one based on fast correlation measure
and three incorporating a DNN-based combiner. We show that significant
performance gains can be achieved by ensembling diverse nets and we investigate
factors contributing to high diversity. Based on the extensive YouTube8M
dataset, we provide an in-depth evaluation and analysis of their behaviour. We
show that the performance of the ensemble is state-of-the-art achieving the
highest accuracy on the YouTube-8M Kaggle test data. The performance of the
ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets,
and show that the resulting method achieves comparable accuracy with
state-of-the-art methods using similar input features
Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach
Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).
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