Classifying Network Data with Deep Kernel Machines

Inspired by a growing interest in analyzing network data, we study the problem of node classification on graphs, focusing on approaches based on kernel machines. Conventionally, kernel machines are linear classifiers in the implicit feature space. We argue that linear classification in the feature space of kernels commonly used for graphs is often not enough to produce good results. When this is the case, one naturally considers nonlinear classifiers in the feature space. We show that repeating this process produces something we call "deep kernel machines." We provide some examples where deep kernel machines can make a big difference in classification performance, and point out some connections to various recent literature on deep architectures in artificial intelligence and machine learning

Machine learning with the hierarchy‐of‐hypotheses (HoH) approach discovers novel pattern in studies on biological invasions

Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context‐dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation‐free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta‐analyses

Comparations of Supervised Machine Learning Techniques in Predicting the Classification of the Household’s Welfare Status

Poverty has been a major problem for most countries around the world, including Indonesia. One approach to eradicate poverty is through equitable distribution of social assistance for target households based on Integrated Database of social assistance. This study has compared several well-known supervised machine learning techniques, namely: Naïve Bayes Classifier, Support Vector Machines, K-Nearest Neighbor Classification, C4.5 Algorithm, and Random Forest Algorithm to predict household welfare status classification by using an Integrated Database as a study case. The main objective of this study was to choose the best-supervised machine learning approach in predicting the classification of household’s welfare status based on attributes in the Integrated Database. The results showed that the Random Forest Algorithm was the best

Are Microcontrollers Ready for Deep Learning-Based Human Activity Recognition?

The last decade has seen exponential growth in the field of deep learning with deep learning on microcontrollers a new frontier for this research area. This paper presents a case study about machine learning on microcontrollers, with a focus on human activity recognition using accelerometer data. We build machine learning classifiers suitable for execution on modern microcontrollers and evaluate their performance. Specifically, we compare Random Forests (RF), a classical machine learning technique, with Convolutional Neural Networks (CNN), in terms of classification accuracy and inference speed. The results show that RF classifiers achieve similar levels of classification accuracy while being several times faster than a small custom CNN model designed for the task. The RF and the custom CNN are also several orders of magnitude faster than state-of-the-art deep learning models. On the one hand, these findings confirm the feasibility of using deep learning on modern microcontrollers. On the other hand, they cast doubt on whether deep learning is the best approach for this application, especially if high inference speed and, thus, low energy consumption is the key objective