Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data

Who’s Watching TV?

This work addresses the problem of “user disambiguation”—estimating the likelihood of each member of a small group using a shared account or device. The specific focus is on television set-top box (STB) viewership data in multiperson households, in which it is impossible to tell with certainty which household members watch what. We formulate user disambiguation as a predictive problem and develop a solution for estimating the likelihood that each individual in a multiperson household watches each TV segment. This method learns priors for viewership in single-person households and then adapts them to the specifics of each multiperson household’s viewership history. We formalize two ad hoc heuristics that are currently used in industry (and research) for estimating audience composition of STB data and conduct a comparative analysis using three data sources: simulated data, real large-scale viewership data, and fully labeled panel data. The results show that our method has superior performance. This approach has practical value for both advertisers and researchers who seek better understanding of TV viewership. It also has applications beyond TV advertising, such as detecting the sharing of streaming passwords among multiple households or any other situation in which multiple users share devices or accounts.

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and there exists a positive link between the model’s prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.

Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning

Accurate spatial distribution information of native, mixed, and tame grasslands is essential for maintaining ecosystem health in the Prairie. This research aimed to use the latest monitoring technology to assess the remaining grasslands in Saskatchewan’s mixed grassland ecoregion (MGE). The classification approach was based on 78 raster-based variables derived from big remote sensing data of multispectral optical space-borne sensors such as MODIS and Sentinel-2, and synthetic aperture radar (SAR) space-borne sensors such as Sentinel-1. Principal component analysis (PCA) was used as a data dimensionality reduction technique to mitigate big data load and improve processing time. Random Forest (RF) was used in the classification process and incorporated the selected variables from 78 satellite-based layers and 2385 reference training points. Within the MGE, the overall accuracy of the classification was 90.2%. Native grassland had 98.20% of user’s accuracy and 88.40% producer’s accuracy, tame grassland had 81.4% user’s accuracy and 93.8% producer’s accuracy, whereas mixed grassland class had very low user’s accuracy (45.8%) and producer’s accuracy 82.83%. Approximately 3.46 million hectares (40.2%) of the MGE area are grasslands (33.9% native, 4% mixed, and 2.3% tame). This study establishes a novel analytical framework for reliable grassland mapping using big data, identifies future challenges, and provides valuable information for Saskatchewan and North America decision-makers.

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

Deep Learning on Big, Sparse, Behavioral Data

The outstanding performance of deep learning (DL) for computer vision and natural language processing has fueled increased interest in applying these algorithms more broadly in both research and practice. This study investigates the application of DL techniques to classification of large sparse behavioral data-which has become ubiquitous in the age of big data collection. We report on an extensive search through DL architecture variants and compare the predictive performance of DL with that of carefully regularized logistic regression (LR), which previously (and repeatedly) has been found to be the most accurate machine learning technique generally for sparse behavioral data. At a high level, we demonstrate that by following recommendations from the literature, researchers and practitioners who are not DL experts can achieve world-class performance using DL. More specifically, we report several findings. As a main result, applying DL on 39 big sparse behavioral classification tasks demonstrates a significant performance improvement compared with LR. A follow-up result suggests that if one were to choose the best shallow technique (rather than just LR), there still would often be an improvement from using DL, but that in this case the magnitude of the improvement might not justify the high cost. Investigating when DL performs better, we find that worse performance is obtained for data sets with low signal-from-noise separability-in line with prior results comparing linear and nonlinear classifiers. Exploring why the deep architectures work well, we show that using the first-layer features learned by DL yields better generalization performance for a linear model than do unsupervised feature-reduction methods (e.g., singular-value decomposition). However, to do well enough to beat well-regularized LR with the original sparse representation, more layers from the deep distributed architecture are needed. With respect to interpreting how deep models come to their decisions, we demonstrate how the neurons on the lowest layer of the deep architecture capture nuances from the raw fine-grained features and allow intuitive interpretation. Looking forward, we propose the use of instance-level counterfactual explanations to gain insight into why deep models classify individual data instances the way they do.