In many real-world applications, data are represented by multiple instances and simultaneously associated with multiple labels. These data are always redundant and generally contaminated by different noise levels. As a result, several machine learning models fail to achieve good classification and find an optimal mapping. Feature selection, instance selection, and label selection are three effective dimensionality reduction techniques. Nevertheless, the literature was limited to feature and/or instance selection but has, to some extent, neglected label selection, which also plays an essential role in the preprocessing step, as label noises can adversely affect the performance of the underlying learning algorithms. In this article, we propose a novel framework termed multilabel Feature Instance Label Selection (mFILS) that simultaneously performs feature, instance, and label selections in both convex and nonconvex scenarios. To the best of our knowledge, this article offers, for the first time ever, a study using the triple and simultaneous selection of features, instances, and labels based on convex and nonconvex penalties in a multilabel scenario. Experimental results are built on some known benchmark datasets to validate the effectiveness of the proposed mFILS.

Feature selection is a very important part of successful data mining applications, because it contributes to the simplification and explainability of the resulting model through dimensionality reduction. Feature selection methods exist for semi-supervised scenarios, in which a small subset of labeled instances is combined with a large batch of unlabeled instances to improve the learning performance. In a multi-label setting, feature selection identifies important features for all labels at once. However, some labels can also be irrelevant and then deteriorate feature selection. In a robust learning framework, removing these noisy labels can decrease the generalization error for the resulting regression model. In this work, we present a novel algorithm performing multi-label semi-supervised feature selection and label seleciton, and show its effectiveness on some publicly available datasets.

In this paper, we focus on co-selection of instances and features in the semi-supervised learning scenario. In this context, co-selection becomes a more challenging problem as data contain labeled and unlabeled examples sampled from the same population. To carry out such semi-supervised co-selection, we propose a unified framework, called sCOs, which efficiently integrates labeled and unlabeled parts into the co-selection process. The framework is based on introducing both asparse regularization termand asimilarity preserving approach. It evaluates the usefulness of features and instances in order to select the most relevant ones, simultaneously. We propose two efficient algorithms that work for both convex and nonconvex functions. To the best of our knowledge, this paper offers, for the first time ever, a study utilizing nonconvex penalties for the co-selection of semi-supervised learning tasks. Experimental results on some known benchmark datasets are provided for validating sCOs and comparing it with some representative methods in the state-of-the art.

Traffic congestion is a phenomenon with which most drivers are familiar. It negatively affects drivers, city's residents and also the economic efficiency. Road pavement condition is one of the main causes of traffic congestion. Warning drivers of road pavement conditions is an effective and highly recommended solution to prevent a traffic jam before it actually occurs. Through this paper, we present a new strategy to reduce traffic congestion. We propose an unsupervised anomaly detection framework that predicts road obstacles. We suggest a well-motivated combination between the Long Short Term Memory (LSTM) and Local outlier value factor (LOF) techniques for predicting obstacles in time series. This combination can potentially offer very promising results in terms of prediction accuracy. We illustrate significant performance gains achieved by our technique with respect to the conventional methods.

In this paper, we aim to improve the performance, time complexity and energy efficiency of deep convolutional neural networks (CNNs) by combining hardware and specialization techniques. Since the pooling step represents a process that contributes significantly to CNNs performance improvement, we propose the Mode-Fisher (MF) pooling method. This form of pooling can potentially offer a very promising results in terms of improving feature extraction performance. The proposed method reduces significantly the data movement in the CNN and save up to 10% of total energy, without any performance penalty.