Food Recognition using Fusion of Classifiers based on CNNs

With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional neural networks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models

Evolving an optimal decision template for combining classifiers.

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets like those having imbalanced data. Moreover, point estimation by computing the average value on the outputs of base classifiers in the Decision Template method is sometimes not a good representation, especially for skewed datasets. Here we propose to search for an optimal decision template in the combining algorithm for a heterogeneous ensemble. To do this, we first generate the base classifier by training the pre-selected learning algorithms on the given training set. The meta-data of the training set is then generated via cross validation. Using the Artificial Bee Colony algorithm, we search for the optimal template that minimizes the empirical 0–1 loss function on the training set. The class label is assigned to the unlabeled sample based on the maximum of the similarity between the optimal decision template and the sample’s meta-data. Experiments conducted on the UCI datasets demonstrated the superiority of the proposed method over several benchmark algorithms

An ensemble of epoch-wise empirical Bayes for few-shot learning

Ministry of Education, Singapore under its Academic Research Funding Tier

Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy

BACKGROUND: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. PRINCIPAL FINDINGS: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. CONCLUSION: The combined EuResist prediction engine is freely available at http://engine.euresist.org

Improved Weighted Random Forest for Classification Problems

Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models. The key to make well-performing ensemble model is in the diversity of the base models. Of the most common solutions for introducing diversity into the decision trees are bagging and random forest. Bagging enhances the diversity by sampling with replacement and generating many training data sets, while random forest adds selecting a random number of features as well. This has made the random forest a winning candidate for many machine learning applications. However, assuming equal weights for all base decision trees does not seem reasonable as the randomization of sampling and input feature selection may lead to different levels of decision-making abilities across base decision trees. Therefore, we propose several algorithms that intend to modify the weighting strategy of regular random forest and consequently make better predictions. The designed weighting frameworks include optimal weighted random forest based on ac-curacy, optimal weighted random forest based on the area under the curve (AUC), performance-based weighted random forest, and several stacking-based weighted random forest models. The numerical results show that the proposed models are able to introduce significant improvements compared to regular random forest

The first Automatic Translation Memory Cleaning Shared Task

This is an accepted manuscript of an article published by Springer in Machine Translation on 21/01/2017, available online: https://doi.org/10.1007/s10590-016-9183-x The accepted version of the publication may differ from the final published version.This paper reports on the organization and results of the rst Automatic Translation Memory Cleaning Shared Task. This shared task is aimed at nding automatic ways of cleaning translation memories (TMs) that have not been properly curated and thus include incorrect translations. As a follow up of the shared task, we also conducted two surveys, one targeting the teams participating in the shared task, and the other one targeting professional translators. While the researchers-oriented survey aimed at gathering information about the opinion of participants on the shared task, the translators-oriented survey aimed to better understand what constitutes a good TM unit and inform decisions that will be taken in future editions of the task. In this paper, we report on the process of data preparation and the evaluation of the automatic systems submitted, as well as on the results of the collected surveys

Prediction of survival probabilities with Bayesian Decision Trees

Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application

Identify error-sensitive patterns by decision tree

© Springer International Publishing Switzerland 2015. When errors are inevitable during data classification, finding a particular part of the classification model which may be more susceptible to error than others, when compared to finding an Achilles’ heel of the model in a casual way, may help uncover specific error-sensitive value patterns and lead to additional error reduction measures. As an initial phase of the investigation, this study narrows the scope of problem by focusing on decision trees as a pilot model, develops a simple and effective tagging method to digitize individual nodes of a binary decision tree for node-level analysis, to link and track classification statistics for each node in a transparent way, to facilitate the identification and examination of the potentially “weakest” nodes and error-sensitive value patterns in decision trees, to assist cause analysis and enhancement development. This digitization method is not an attempt to re-develop or transform the existing decision tree model, but rather, a pragmatic node ID formulation that crafts numeric values to reflect the tree structure and decision making paths, to expand post-classification analysis to detailed node-level. Initial experiments have shown successful results in locating potentially high-risk attribute and value patterns; this is an encouraging sign to believe this study worth further exploration

Decision level ensemble method for classifying multi-media data

In the digital era, the data, for a given analytical task, can be collected in different formats, such as text, images and audio etc. The data with multiple formats are called multimedia data. Integrating and fusing multimedia datasets has become a challenging task in machine learning and data mining. In this paper, we present heterogeneous ensemble method that combines multi-media datasets at the decision level. Our method consists of several components, including extracting the features from multimedia datasets that are not represented by features, modelling independently on each of multimedia datasets, selecting models based on their accuracy and diversity and building the ensemble at the decision level. Hence our method is called decision level ensemble method (DLEM). The method is tested on multimedia data and compared with other heterogeneous ensemble based methods. The results show that the DLEM outperformed these methods significantly

Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods