Exploring label correlations using decision templates for ensemble of classifier chains
Victor F. Rocha Department of Computer Science, Federal University of Esp\' rito Santo, Vit\' o ria, Esp\' rito Santo, 29075-910, Brazil Alexandre L. Rodrigues Department of Statistics, Federal University of Esp\' rito Santo, Vit\' o ria, Esp\' rito Santo, 29075-910, Brazil Thiago Oliveira-Santos Department of Computer Science, Federal University of Esp\' rito Santo, Vit\' o ria, Esp\' rito Santo, 29075-910, Brazil Fl\' a vio M. Varej\ a o Department of Computer Science, Federal University of Esp\' rito Santo, Vit\' o ria, Esp\' rito Santo, 29075-910, Brazil Corresponding author: Victor F. Rocha
Victor F. Rocha, Alexandre L. Rodrigues, Thiago Oliveira-Santos, Flávio M. Varejão
The use of ensemble-based multi-label methods has been shown to be effective in improving multi-label classification results. One of the most widely used ensemble-based multi-label classifiers is Ensemble of Classifier Chains. Decision templates for Ensemble of Classifier Chains (DTECC) is a fusion scheme based on Decision Templates that combines the predictions of Ensemble of Classifier Chains using information from the decision profile for each label, without considering information about other labels that might contribute to the classified result. Based on DTECC, this work proposes the Unconditionally Dependent Decision Templates for Ensemble of Classifier Chains (UDDTECC) method, a classifier fusion method that seeks to exploit correlations between labels in the fusion process. In this way, the classification of each label in the problem takes into account the label values that are considered conditionally dependent and that can lead to an improvement in the classification performance. The proposed method is experimentally compared with two traditional classifier fusion strategies and with a stacking-based strategy. Empirical evidence shows that using the proposed Decision Templates adaptation can improve the performance compared to the traditionally used fusion schemes on most of the evaluated metrics.
University of Florida（佛罗里达大学）；National Cheng Kung University（国立成功大学）；University of Central Florida（中佛罗里达大学）
Machine learning researchers have long noticed the phenomenon that the model training process will be more effective and efficient when the training samples are densely sampled around the underlying decision boundary. While this observation has already been widely applied in a range of machine learning security techniques, it lacks theoretical analyses of the correctness of the observation. To address this challenge, we first add particular perturbation to original training examples using adversarial attack methods so that the generated examples could lie approximately on the decision boundary…
University of Pennsylvania（宾夕法尼亚大学）；Vanderbilt University（范德堡大学）；Children’s Hospital of Philadelphia（费城儿童医院）
Long horizon robot learning tasks with sparse rewards pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging control tasks is that they often receive expert intervention that enables them to understand the high-level structure of the task before mastering low-level control actions. We propose a framework for leveraging expert intervention to solve long-horizon reinforcement learning tasks. We consider \emph{option templates}, which are specifications encoding a potential option that can be trained using reinforcement learn…
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models
Bosch Center for Artificial Intelligence, Robert Bosch GmbH（博世人工智能中心，罗伯特·博世公司）；Bosch Center for Artificial Intelligence, Robert Bosch LLC（博世人工智能中心，罗伯特·博世有限责任公司）
Designing effective and efficient classifier for pattern analysis is a key problem in machine learning and computer vision. Many the solutions to the problem require to perform logic operations such as `and', `or', and `not'. Classification and regression tree (CART) include these operations explicitly. Other methods such as neural networks, SVM, and boosting learn/compute a weighted sum on features (weak classifiers), which weakly perform the 'and' and 'or' operations. However, it is hard for these classifiers to deal with the 'xor' pattern directly. In this paper, we propose layered logic cl…
Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；Google AI（谷歌人工智能）
The potential for learned models to amplify existing societal biases has been broadly recognized. Fairness-aware classifier constraints, which apply equality metrics of performance across subgroups defined on sensitive attributes such as race and gender, seek to rectify inequity but can yield non-uniform degradation in performance for skewed datasets. In certain domains, imbalanced degradation of performance can yield another form of unintentional bias. In the spirit of constructing fairness-aware algorithms as societal imperative, we explore an alternative: Pareto-Efficient Fairness (PEF). Th…
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
Alibaba Group（阿里巴巴集团）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；Princeton University（普林斯顿大学）；Peking University（北京大学）；Meta；Nanjing University（南京大学）
College of Information Sciences and Technology, The Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）；Department of Computer Science, College of Computing, Illinois Institute of Technology（伊利诺伊理工学院计算学院计算机科学系）
Despite the rapid development and great success of machine learning models, extensive studies have exposed their disadvantage of inheriting latent discrimination and societal bias from the training data. This phenomenon hinders their adoption on high-stake applications. Thus, many efforts have been taken for developing fair machine learning models. Most of them require that sensitive attributes are available during training to learn fair models. However, in many real-world applications, it is usually infeasible to obtain the sensitive attributes due to privacy or legal issues, which challenges…
Kitami Institute of Technology（北见工业大学）；Samurai Labs（武士实验室）
In this research. we analyze the potential of Feature Density (HD) as a way to comparatively estimate machine learning (ML) classifier performance prior to training. The goal of the study is to aid in solving the problem of resource-intensive training of ML models which is becoming a serious issue due to continuously increasing dataset sizes and the ever rising popularity of Deep Neural Networks (DNN). The issue of constantly increasing demands for more powerful computational resources is also affecting the environment, as training large-scale ML models are causing alarmingly-growing amounts o…
National Tsing Hua University（国立清华大学）；Konkuk University（建国大学）；University of Washington（华盛顿大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）
The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applie…
Machine learning (ML) pipeline composition and optimisation have been studied to seek multi-stage ML models, i.e. preprocessor-inclusive, that are both valid and well-performing. These processes typically require the design and traversal of complex configuration spaces consisting of not just individual ML components and their hyperparameters, but also higher-level pipeline structures that link these components together. Optimisation efficiency and resulting ML-model accuracy both suffer if this pipeline search space is unwieldy and excessively large; it becomes an appealing notion to avoid cos…
Northwestern University（西北大学）；Samsung Research America（三星美国研究院）
Cough is a major symptom of respiratory-related diseases. There exists a tremendous amount of work in detecting coughs from audio but there has been no effort to identify coughs from solely inertial measurement unit (IMU). Coughing causes motion across the whole body and especially on the neck and head. Therefore, head motion data during coughing captured by a head-worn IMU sensor could be leveraged to detect coughs using a template matching algorithm. In time series template matching problems, K-Nearest Neighbors (KNN) combined with elastic distance measurement (esp. Dynamic Time Warping (DTW…
Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation
School of Computer Science and Engineering（计算机科学与工程学院）；Vellore Institute of Technology（韦洛尔理工学院）
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY ALLAHABAD（印度阿拉哈巴德信息技术学院）
Accuracy is the most important parameter among few others which defines the effectiveness of a machine learning algorithm. Higher accuracy is always desirable. Now, there is a vast number of well established learning algorithms already present in the scientific domain. Each one of them has its own merits and demerits. Merits and demerits are evaluated in terms of accuracy, speed of convergence, complexity of the algorithm, generalization property, and robustness among many others. Also the learning algorithms are data-distribution dependent. Each learning algorithm is suitable for a particular…
This paper tackles one of the greatest limitations in Machine Learning: Data Scarcity. Specifically, we explore whether high accuracy classifiers can be built from small datasets, utilizing a combination of data augmentation techniques and machine learning algorithms. In this paper, we experiment with Easy Data Augmentation (EDA) and Backtranslation, as well as with three popular learning algorithms, Logistic Regression, Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory Network (Bi-LSTM). For our experimentation, we utilize the Wikipedia Toxic Comments dataset so that in t…
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）
Adaptation of a classifier to new domains is one of the challenging problems in machine learning. This has been addressed using many deep and non-deep learning based methods. Among the methodologies used, that of adversarial learning is widely applied to solve many deep learning problems along with domain adaptation. These methods are based on a discriminator that ensures source and target distributions are close. However, here we suggest that rather than using a point estimate obtaining by a single discriminator, it would be useful if a distribution based on ensembles of discriminators could …
National Institute of Technology Silchar（西尔查尔国家理工学院）；Indian Institute of Technology, Indore（印多尔印度理工学院）
Structural alterations have been thoroughly investigated in the brain during the early onset of schizophrenia (SCZ) with the development of neuroimaging methods. The objective of the paper is an efficient classification of SCZ in 2 different classes: Cognitive Normal (CN), and SCZ using magnetic resonance imaging (MRI) images. This paper proposed a lightweight 3D convolutional neural network (CNN) based framework for SCZ diagnosis using MRI images. In the proposed model, lightweight 3D CNN is used to extract both spatial and spectral features simultaneously from 3D volume MRI scans, and classi…
Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification o…
