Automated Model Selection with AMSFin a production process of the automotive industry

Machine learning, statistics and knowledge engineering provide a broad variety of supervised learning algorithms for classification. In this paper we introduce the Automated Model Selection Framework (AMSF) which presents automatic and semi-automatic methods to select classifiers. To achieve this we split up the selection process into three distinct phases. Two of those select algorithms by static rules which are derived from a manually created knowledgebase. At this stage of AMSF the user can choose between different rankers in the third phase. Currently, we use instance based learning and a scoring scheme for ranking the classifiers. After evaluation of different rankers we will recommend the most successful to the user by default. Besides describing the architecture and design issues, we additionally point out the versatile ways AMSF is applied in a production process of the automotive industr

Mask-guided sample selection for Semi-Supervised Instance Segmentation

Image segmentation methods are usually trained with pixel-level annotations, which require significant human effort to collect. The most common solution to address this constraint is to implement weakly-supervised pipelines trained with lower forms of supervision, such as bounding boxes or scribbles. Another option are semi-supervised methods, which leverage a large amount of unlabeled data and a limited number of strongly-labeled samples. In this second setup, samples to be strongly-annotated can be selected randomly or with an active learning mechanism that chooses the ones that will maximize the model performance. In this work, we propose a sample selection approach to decide which samples to annotate for semi-supervised instance segmentation. Our method consists in first predicting pseudo-masks for the unlabeled pool of samples, together with a score predicting the quality of the mask. This score is an estimate of the Intersection Over Union (IoU) of the segment with the ground truth mask. We study which samples are better to annotate given the quality score, and show how our approach outperforms a random selection, leading to improved performance for semi-supervised instance segmentation with low annotation budgets.Comment: Preprint submitted to Multimedia Tools and Application

Learning with Low-Quality Data: Multi-View Semi-Supervised Learning with Missing Views

The focus of this thesis is on learning approaches for what we call ``low-quality data'' and in particular data in which only small amounts of labeled target data is available. The first part provides background discussion on low-quality data issues, followed by preliminary study in this area. The remainder of the thesis focuses on a particular scenario: multi-view semi-supervised learning. Multi-view learning generally refers to the case of learning with data that has multiple natural views, or sets of features, associated with it. Multi-view semi-supervised learning methods try to exploit the combination of multiple views along with large amounts of unlabeled data in order to learn better predictive functions when limited labeled data is available. However, lack of complete view data limits the applicability of multi-view semi-supervised learning to real world data. Commonly, one data view is readily and cheaply available, but additionally views may be costly or only available in some cases. This thesis work aims to make multi-view semi-supervised learning approaches more applicable to real world data specifically by addressing the issue of missing views through both feature generation and active learning, and addressing the issue of model selection for semi-supervised learning with limited labeled data. This thesis introduces a unified approach for handling missing view data in multi-view semi-supervised learning tasks, which applies to both data with completely missing additional views and data only missing views in some instances. The idea is to learn a feature generation function mapping one view to another with the mapping biased to encourage the features generated to be useful for multi-view semi-supervised learning algorithms. The mapping is then used to fill in views as pre-processing. Unlike previously proposed single-view multi-view learning approaches, the proposed approach is able to take advantage of additional view data when available, and for the case of partial view presence is the first feature-generation approach specifically designed to take into account the multi-view semi-supervised learning aspect. The next component of this thesis is the analysis of an active view completion scenario. In some tasks, it is possible to obtain missing view data for a particular instance, but with some associated cost. Recent work has shown an active selection strategy can be more effective than a random one. In this thesis, a better understanding of active approaches is sought, and it is demonstrated that the effectiveness of an active selection strategy over a random one can depend on the relationship between the views. Finally, an important component of making multi-view semi-supervised learning applicable to real world data is the task of model selection, an open problem which is often avoided entirely in previous work. For cases of very limited labeled training data the commonly used cross-validation approach can become ineffective. This thesis introduces a re-training alternative to the method-dependent approaches similar in motivation to cross-validation, that involves generating new training and test data by sampling from the large amount of unlabeled data and estimated conditional probabilities for the labels. The proposed approaches are evaluated on a variety of multi-view semi-supervised learning data sets, and the experimental results demonstrate their efficacy

Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach

The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergence related issues is proposed. Additionally, to reduce the impact of translation errors and handle instance selection problem, we propose a clustering-based bee-colony-sample selection method for the optimal selection of most distinguishing features representing the target data. To evaluate the proposed model, various experiments are conducted employing an English-Arabic cross-lingual data set. Simulations results demonstrate that the proposed model outperforms the baseline approaches in terms of classification performances. Furthermore, the statistical outcomes indicate the advantages of the proposed training data sampling and target-based feature selection to reduce the negative effect of translation errors. These results highlight the fact that the proposed approach achieves a performance that is close to in-language supervised models

Unsupervised feature learning with discriminative encoder

In recent years, deep discriminative models have achieved extraordinary performance on supervised learning tasks, significantly outperforming their generative counterparts. However, their success relies on the presence of a large amount of labeled data. How can one use the same discriminative models for learning useful features in the absence of labels? We address this question in this paper, by jointly modeling the distribution of data and latent features in a manner that explicitly assigns zero probability to unobserved data. Rather than maximizing the marginal probability of observed data, we maximize the joint probability of the data and the latent features using a two step EM-like procedure. To prevent the model from overfitting to our initial selection of latent features, we use adversarial regularization. Depending on the task, we allow the latent features to be one-hot or real-valued vectors and define a suitable prior on the features. For instance, one-hot features correspond to class labels and are directly used for the unsupervised and semi-supervised classification task, whereas real-valued feature vectors are fed as input to simple classifiers for auxiliary supervised discrimination tasks. The proposed model, which we dub discriminative encoder (or DisCoder), is flexible in the type of latent features that it can capture. The proposed model achieves state-of-the-art performance on several challenging tasks.Comment: 10 pages, 4 figures, International Conference on Data Mining, 201

Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition

Contrastive learning has been applied to Human Activity Recognition (HAR) based on sensor data owing to its ability to achieve performance comparable to supervised learning with a large amount of unlabeled data and a small amount of labeled data. The pre-training task for contrastive learning is generally instance discrimination, which specifies that each instance belongs to a single class, but this will consider the same class of samples as negative examples. Such a pre-training task is not conducive to human activity recognition tasks, which are mainly classification tasks. To address this problem, we follow SimCLR to propose a new contrastive learning framework that negative selection by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples. We evaluate ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR, using mean F1-score as the evaluation metric. The experiment results show that it outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.Comment: 11 pages, 5 figure

InstanT: Semi-supervised Learning with Instance-dependent Thresholds

Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning pseudo-labels to confident unlabeled instances and incorporating them into the training set. Therefore, the selection criteria of confident instances are crucial to the success of SSL. Recently, there has been growing interest in the development of SSL methods that use dynamic or adaptive thresholds. Yet, these methods typically apply the same threshold to all samples, or use class-dependent thresholds for instances belonging to a certain class, while neglecting instance-level information. In this paper, we propose the study of instance-dependent thresholds, which has the highest degree of freedom compared with existing methods. Specifically, we devise a novel instance-dependent threshold function for all unlabeled instances by utilizing their instance-level ambiguity and the instance-dependent error rates of pseudo-labels, so instances that are more likely to have incorrect pseudo-labels will have higher thresholds. Furthermore, we demonstrate that our instance-dependent threshold function provides a bounded probabilistic guarantee for the correctness of the pseudo-labels it assigns.Comment: Accepted as poster for NeurIPS 202