4,081 research outputs found
Ensemble of convolutional neural networks to improve animal audio classification
Abstract In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of producing better classification accuracy than other state-of-the-art approaches without ad hoc parameter optimization. We present an ensemble of classifiers that performs competitively on different types of animal audio datasets using the same set of classifiers and parameter settings. To produce this general-purpose ensemble, we ran a large number of experiments that fine-tuned pretrained convolutional neural networks (CNNs) for different audio classification tasks (bird, bat, and whale audio datasets). Six different CNNs were tested, compared, and combined. Moreover, a further CNN, trained from scratch, was tested and combined with the fine-tuned CNNs. To the best of our knowledge, this is the largest study on CNNs in animal audio classification. Our results show that several CNNs can be fine-tuned and fused for robust and generalizable audio classification. Finally, the ensemble of CNNs is combined with handcrafted texture descriptors obtained from spectrograms for further improvement of performance. The MATLAB code used in our experiments will be provided to other researchers for future comparisons at https://github.com/LorisNanni
CIFAR-10: KNN-based Ensemble of Classifiers
In this paper, we study the performance of different classifiers on the
CIFAR-10 dataset, and build an ensemble of classifiers to reach a better
performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and
Convolutional Neural Network (CNN), on some classes, are mutually exclusive,
thus yield in higher accuracy when combined. We reduce KNN overfitting using
Principal Component Analysis (PCA), and ensemble it with a CNN to increase its
accuracy. Our approach improves our best CNN model from 93.33% to 94.03%
Multi-Label Classifier Chains for Bird Sound
Bird sound data collected with unattended microphones for automatic surveys,
or mobile devices for citizen science, typically contain multiple
simultaneously vocalizing birds of different species. However, few works have
considered the multi-label structure in birdsong. We propose to use an ensemble
of classifier chains combined with a histogram-of-segments representation for
multi-label classification of birdsong. The proposed method is compared with
binary relevance and three multi-instance multi-label learning (MIML)
algorithms from prior work (which focus more on structure in the sound, and
less on structure in the label sets). Experiments are conducted on two
real-world birdsong datasets, and show that the proposed method usually
outperforms binary relevance (using the same features and base-classifier), and
is better in some cases and worse in others compared to the MIML algorithms.Comment: 6 pages, 1 figure, submission to ICML 2013 workshop on bioacoustics.
Note: this is a minor revision- the blind submission format has been replaced
with one that shows author names, and a few corrections have been mad
Dissimilarity-based Ensembles for Multiple Instance Learning
In multiple instance learning, objects are sets (bags) of feature vectors
(instances) rather than individual feature vectors. In this paper we address
the problem of how these bags can best be represented. Two standard approaches
are to use (dis)similarities between bags and prototype bags, or between bags
and prototype instances. The first approach results in a relatively
low-dimensional representation determined by the number of training bags, while
the second approach results in a relatively high-dimensional representation,
determined by the total number of instances in the training set. In this paper
a third, intermediate approach is proposed, which links the two approaches and
combines their strengths. Our classifier is inspired by a random subspace
ensemble, and considers subspaces of the dissimilarity space, defined by
subsets of instances, as prototypes. We provide guidelines for using such an
ensemble, and show state-of-the-art performances on a range of multiple
instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning
Systems, Special Issue on Learning in Non-(geo)metric Space
Classification of time series by shapelet transformation
Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best shapelets, which are used to produce a transformed dataset, where each of the features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following knowledge, which we call the \emph{feature sign}---whether or not
a particular feature has on average stronger values over positive samples than
over negatives. We show how this can be estimated using as few as a single
labeled training sample per class. Then, using these feature signs, we extend
an initial supervised learning problem into an (almost) unsupervised clustering
formulation that can incorporate new data without requiring ground truth
labels. Our method works both as a feature selection mechanism and as a fully
competitive classifier. It has important properties, low computational cost and
excellent accuracy, especially in difficult cases of very limited training
data. We experiment on large-scale recognition in video and show superior speed
and performance to established feature selection approaches such as AdaBoost,
Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.Comment: arXiv admin note: text overlap with arXiv:1411.771
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD), which is the problem of learning
detectors using only image-level labels, has been attracting more and more
interest. However, this problem is quite challenging due to the lack of
location supervision. To address this issue, this paper integrates saliency
into a deep architecture, in which the location in- formation is explored both
explicitly and implicitly. Specifically, we select highly confident object pro-
posals under the guidance of class-specific saliency maps. The location
information, together with semantic and saliency information, of the selected
proposals are then used to explicitly supervise the network by imposing two
additional losses. Meanwhile, a saliency prediction sub-network is built in the
architecture. The prediction results are used to implicitly guide the
localization procedure. The entire network is trained end-to-end. Experiments
on PASCAL VOC demonstrate that our approach outperforms all state-of-the-arts.Comment: Accepted to appear in IJCAI 201
Ensembles of wrappers for automated feature selection in fish age classification
In feature selection, the most important features must be chosen so as to decrease the number thereof while retaining their discriminatory information. Within this context, a novel feature selection method based on an ensemble of wrappers is proposed and applied for automatically select features in fish age classification. The effectiveness of this procedure using an Atlantic cod database has been tested for different powerful statistical learning classifiers. The subsets based on few features selected, e.g. otolith weight and fish weight, are particularly noticeable given current biological findings and practices in fishery research and the classification results obtained with them outperforms those of previous studies in which a manual feature selection was performed.Peer ReviewedPostprint (author's final draft
Software defect prediction: do different classifiers find the same defects?
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio
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