Modern neural networks have recently been found to be poorly calibrated, primarily in the direction of over-confidence. Methods like entropy penalty and temperature smoothing improve calibration by clamping confidence, but in doing so compromise the many legitimately confident predictions. We propose a more principled fix that minimizes an explicit calibration error during training. We present MMCE, a RKHS kernel based measure of calibration that is efficiently trainable alongside the negative likelihood loss without careful hyperparameter tuning. Theoretically too, MMCE is a sound measure of calibration that is minimized at perfect calibration, and whose finite sample estimates are consistent and enjoy fast convergence rates. Extensive experiments on several network architectures demonstrate that MMCE is a fast, stable, and accurate method to minimize calibration error metrics while maximally preserving the number of high confidence predictions.

/pdf/trainable-calibration-measures-for-neural-networks-from-4ovxjdawdm.pdf

Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings

The use of Convolutional Neural Networks (CNNs) as a feature learning method for Human Activity Recognition (HAR) is becoming more and more common. Unlike conventional machine learning methods, which require domain-specific expertise, CNNs can extract features automatically. On the other hand, CNNs require a training phase, making them prone to the cold-start problem. In this work, a case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions. The case study consists of two main steps: (1) different topologies and parameters are assessed to identify the best candidate models for HAR, thus obtaining a pre-trained CNN model. The pre-trained model (2) is then employed as feature extractor evaluating its use with a large scale real-world dataset. Two CNN applications were considered: Inertial Measurement Unit (IMU) and audio based HAR. For the IMU data, balanced accuracy was 91.98% on the UCI-HAR dataset, and 67.51% on the real-world Extrasensory dataset. For the audio data, the balanced accuracy was 92.30% on the DCASE 2017 dataset, and 35.24% on the Extrasensory dataset.

/pdf/feature-learning-for-human-activity-recognition-using-ksvscqdwpo.pdf

Feature learning for Human Activity Recognition using Convolutional Neural Networks: A case study for Inertial Measurement Unit and Audio data

The self-regulated recognition of human activities from time-series smartphone sensor data is a growing research area in smart and intelligent health care. Deep learning (DL) approaches have exhibited improvements over traditional machine learning (ML) models in various domains, including human activity recognition (HAR). Several issues are involved with traditional ML approaches; these include handcrafted feature extraction, which is a tedious and complex task involving expert domain knowledge, and the use of a separate dimensionality reduction module to overcome overfitting problems and hence provide model generalization. In this article, we propose a DL-based approach for activity recognition with smartphone sensor data, i.e., accelerometer and gyroscope data. Convolutional neural networks (CNNs), autoencoders (AEs), and long short-term memory (LSTM) possess complementary modeling capabilities, as CNNs are good at automatic feature extraction, AEs are used for dimensionality reduction and LSTMs are adept at temporal modeling. In this study, we take advantage of the complementarity of CNNs, AEs, and LSTMs by combining them into a unified architecture. We explore the proposed architecture, namely, “ConvAE-LSTM”, on four different standard public datasets (WISDM, UCI, PAMAP2, and OPPORTUNITY). The experimental results indicate that our novel approach is practical and provides relative smartphone-based HAR solution performance improvements in terms of computational time, accuracy, F1-score, precision, and recall over existing state-of-the-art methods. 

ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition

An Integration of feature extraction and Guided Regularized Random Forest feature selection for Smartphone based Human Activity Recognition

Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets.

/pdf/adversarial-approaches-to-tackle-imbalanced-data-in-machine-3oms4zd3.pdf

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

In Human Activity Recognition (HAR), supervised Machine Learning methods are predominantly used, making availability of datasets a major issue for research in the field. In particular, the majority of available datasets are collected under controlled conditions. Consequently, models trained under similar circumstances, generally exhibit a significant decrease in recognition accuracy when they are moved to final deployment in the wild, within unconstrained settings. This paper presents a new dataset for HAR, collected in free-living and unconstrained conditions. 10 subjects were recruited for the purpose of data collection. Data was recorded over a 6 week period using a smartphone app, and a wristband activity monitor. During the first and last week of observation, participants also wore an ActivPAL" activity logger. The data collected have been partially self-labeled by participants, by means of the mobile app provided for data collection. The dataset collected can be used to evaluate HAR algorithm and models in real-world unconstrained settings. Together with the description of the dataset, this work presents some preliminary results, obtained cross-validating a model trained on the publicly available Extrasensory dataset, and testing its performance on our newly collected dataset. Results obtained highlighted high cross-subject variability when testing on new subjects, with a balanced accuracy varying between 53.33% and 90.01%, with an average balanced accuracy of 71.73%.

A Public Domain Dataset for Human Activity Recognition in Free-Living Conditions

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A Public Domain Dataset for Human Activity Recognition in Free-Living Conditions