DOCSLIB.ORG

One-Network Adversarial Fairness

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
One-Network Adversarial Fairness One-network Adversarial Fairness Tameem Adel Isabel Valera Zoubin Ghahramani Adrian Weller University of Cambridge, UK MPI-IS, Germany University of Cambridge, UK University of Cambridge, UK [email protected] [email protected] Uber AI Labs, USA The Alan Turing Institute, UK [email protected] [email protected] Abstract is general, in that it may be applied to any differentiable dis- criminative model. We establish a fairness paradigm where There is currently a great expansion of the impact of machine the architecture of a deep discriminative model, optimized learning algorithms on our lives, prompting the need for ob- for accuracy, is modified such that fairness is imposed (the jectives other than pure performance, including fairness. Fair- ness here means that the outcome of an automated decision- same paradigm could be applied to a deep generative model making system should not discriminate between subgroups in future work). Beginning with an ordinary neural network characterized by sensitive attributes such as gender or race. optimized for prediction accuracy of the class labels in a Given any existing differentiable classifier, we make only classification task, we propose an adversarial fairness frame- slight adjustments to the architecture including adding a new work performing a change to the network architecture, lead- hidden layer, in order to enable the concurrent adversarial op- ing to a neural network that is maximally uninformative timization for fairness and accuracy. Our framework provides about the sensitive attributes of the data as well as predic- one way to quantify the tradeoff between fairness and accu- tive of the class labels. In this adversarial learning frame- racy, while also leading to strong empirical performance. work, there is no need for a separate network architecture representing the adversary, thus we avoid the well-known 1 Introduction difficulties which may arise from double-network adversar- ial learning (Goodfellow 2016; Goodfellow et al. 2014). Automated decision support has become widespread, raising We make the following contributions: 1) We propose a concerns about potential unfairness. Here, following earlier fairness algorithm by modifying the architecture of a poten- work, unfairness means discriminating against a particular tially unfair model to simultaneously optimize for both ac- group of people due to sensitive group characteristics such curacy and fairness with respect to sensitive attributes (Sec- as gender or race (Grgic-Hlaca et al. 2018b; Hardt, Price, tion 2); 2) We quantify the tradeoff between the accuracy and Srebro 2016; Kusner et al. 2017; Louizos et al. 2016; and fairness objectives (Section 2); 3) We propose a vari- Zafar et al. 2017c; 2017a; Zemel et al. 2013). Fairness con- ation of the adversarial learning procedure to increase di- cerns have been considered in applications including pre- versity among elements of each minibatch of the gradient dictive policing (Brennan, Dieterich, and Ehret 2009), re- descent training, in order to achieve a representation with cidivism prediction (Chouldechova 2017) and credit scor- higher fidelity. This variation may be of independent inter- ing (Khandani, Kim, and Lo 2010). The current trend in the est since it is applicable generally to adversarial frameworks literature on fairness is to begin with a notion (definition) (Section 2.2); 4) We develop a novel generalization bound of fairness, and then to construct a model which automates for our framework illustrating the theoretical grounding be- the detection and/or eradication of unfairness accordingly. hind the relationship between the label classifier and the ad- Common definitions of fairness consider whether or not a versary’s ability to predict the sensitive attribute value (Sec- decision is related to sensitive attributes, such as gender. tion 3); and 5) We experiment on two fairness datasets com- Most state-of-the-art machine learning algorithms for fair- paring against many earlier approaches to demonstrate state- ness build from scratch a model’s architecture tailored for a of-the-art effectiveness of our methods (Section 4). specific fairness notion. In contrast, we propose a method that slightly modifies the architecture of the model which was to be optimized solely for accuracy. We learn a fair rep- 2 Fair Learning by Modifying resentation together with a new performance function acting an Unfair Model on it, with the goal of concurrently optimizing for both fair- ness and performance (accuracy). Our method is based on an We aim to adapt a classifier that was to learn solely to pre- adversarial framework, which allows explicitly measuring dict the data labels into a fair classifier without reconstruct- the tradeoff between fairness and accuracy. Our approach ing its architecture from scratch. Our strategy is to add a term to the optimization objective to simultaneously maxi- Copyright c 2019, Association for the Advancement of Artificial mize both fairness and accuracy. This term imposes fairness Intelligence (www.aaai.org). All rights reserved. by establishing a fair representation of the input data which is invariant to changes in the sensitive attribute values. To achieving fairness; ii) quantitatively evaluating how much optimize this invariance, an adversary is included at the top fairness we achieve, and; iii) quantifying the impact of such of the model in the form of a classifier which tries to predict fairness on accuracy, i.e. computing the difference in accu- the sensitive attribute value. For a deep model, this can be racy between the proposed fair model and a corresponding implemented via: (i) adding another classifier at the top of (potentially unfair) model. In Figure 2, another schematic di- the network such that we have two classifiers: the original agram of the proposed modifications is displayed. The pre- label predictor and the new predictor of sensitive attribute dictor φ0(x0) depicts a classifier predicting the s value from value(s); and (ii) adding a network layer just under the clas- x0. The ability to accurately predict s given x0 signifies a sifiers (now the top hidden layer) that aims at maximizing high risk of unfairness since this means that the sensitive at- the performance of the label predictor, while minimizing the tributes may be influential in any decision making process performance of the sensitive attribute predictor by reversing based on x0. Adversarially through g, the non-sensitive fea- the gradients of the latter in the backward phase of the gra- tures x are transformed into a representation x0 which at- dient descent training. See Figure 1 for an illustration. tempts to break any dependence on the sensitive attributes s. As such, the optimization objective can have many triv- 2.1 Fair adversarial discriminative (FAD) model ial solutions, e.g. g that maps every x into 0 which provides n no information to predict s. In order to prevent that, and to We consider data D = f(xi; yi; si)gi=1, where xi, yi and ensure that fairness is rather aligned with the accuracy ob- si refer to the input features, the ground truth label and the jective, a classifier f 0 predicts the labels y from x0. Here we sensitive attribute(s), respectively. Denote by y^i the corre- consider classification accuracy to be the metric of the ini- sponding label estimate. Typically speaking, y^i is predicted tial model. However, our adversarial paradigm can also be by a potentially unfair discriminative classifier possibly rep- adopted in models with other metrics to achieve fairness. resented by a neural network whose input is features x and sensitive attributes s. The goal of fairness is to ensure that the prediction of y is not dependent on these attributes s (Zafar et al. 2017c). A naive approach is to simply dis- card s and let classifier f have input x - this has some proponents in terms of process (Grgic-Hlaca et al. 2018a; 2018b) but suffers since x might include significant infor- mation about s. For example, an address attribute in x might give a strong indication about race s. We achieve fairness through the adversarial construction illustrated in Figures 1 and 2, as follows. Define the em- pirical classification loss of the label classifier f 0(x0) as 0 0 1 PjDj 0 0 L(y^; y) = L(f (x ); y) = jDj i=1 Err(f (xi); yi), and similarly the classification loss of the classifier φ0, predict- ing the value of the sensitive attribute s, as L(φ0(x0); s) = Figure 1: Architecture of the proposed fair adversarial dis- criminative model. The parts added, due to FAD, to a po- 1 PjDj Err(φ0(x0); s ). A simplified view of the ar- jDj i=1 i i tentially unfair deep architecture with input x are (shown in chitecture of our fairness adversarial discriminative (FAD) red): i) the layer g where x0 is learned and; 2) the sensitive framework is displayed in Figure 1. We extend the original attribute s predictor φ0 at the top of the network. Denote architecture of the potentially unfair predictor, by adding a by w the weight vector of the respective layer. The forward 0 new layer g that produces a fair data representation x , and pass of backpropagation proceeds normally in FAD. During 0 adding a new predictor φ aiming to predict the sensitive the backward pass (dotted lines), the gradient from the loss attribute s. Denoting by w the weight vector of a network @L((φ0(x0));s) of the s classifier is multiplied with a negative layer, the backpropagation forward pass proceeds normally @wφ0 in FAD. During the backward pass (dotted lines), however, sign so that g adversarially aims at increasing the loss of 0 0 @L(φ0(x0);s) φ , resulting in a representation x maximally invariant to the gradient from the loss of the s classifier is @wφ0 the change in values of s.
Load more

Recommended publications
  • Remote Sensing
    remote sensing Article A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records Guangyuan Zhang 1, Xiaoping Rui 2,* , Stefan Poslad 1, Xianfeng Song 3 , Yonglei Fan 3 and Bang Wu 1 1 School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK; [email protected] (G.Z.); [email protected] (S.P.); [email protected] (B.W.) 2 School of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China 3 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; [email protected] (X.S.); [email protected] (Y.F.) * Correspondence: [email protected] Received: 10 June 2020; Accepted: 8 August 2020; Published: 10 August 2020 Abstract: Estimating and mapping population distributions dynamically at a city-wide spatial scale, including those covering suburban areas, has profound, practical, applications such as urban and transportation planning, public safety warning, disaster impact assessment and epidemiological modelling, which benefits governments, merchants and citizens. More recently, call detail record (CDR) of mobile phone data has been used to estimate human population distributions. However, there is a key challenge that the accuracy of such a method is difficult to validate because there is no ground truth data for the dynamic population density distribution in time scales such as hourly. In this study, we present a simple and accurate method to generate more finely grained temporal-spatial population density distributions based upon CDR data. We designed an experiment to test our method based upon the use of a deep convolutional generative adversarial network (DCGAN).
    [Show full text]
  • On Construction of Hybrid Logistic Regression-Naıve Bayes Model For
    JMLR: Workshop and Conference Proceedings vol 52, 523-534, 2016 PGM 2016 On Construction of Hybrid Logistic Regression-Na¨ıve Bayes Model for Classification Yi Tan & Prakash P. Shenoy [email protected], [email protected] University of Kansas School of Business 1654 Naismith Drive Lawrence, KS 66045 Moses W. Chan & Paul M. Romberg fMOSES.W.CHAN, [email protected] Advanced Technology Center Lockheed Martin Space Systems Sunnyvale, CA 94089 Abstract In recent years, several authors have described a hybrid discriminative-generative model for clas- sification. In this paper we examine construction of such hybrid models from data where we use logistic regression (LR) as a discriminative component, and na¨ıve Bayes (NB) as a generative com- ponent. First, we estimate a Markov blanket of the class variable to reduce the set of features. Next, we use a heuristic to partition the set of features in the Markov blanket into those that are assigned to the LR part, and those that are assigned to the NB part of the hybrid model. The heuristic is based on reducing the conditional dependence of the features in NB part of the hybrid model given the class variable. We implement our method on 21 different classification datasets, and we compare the prediction accuracy of hybrid models with those of pure LR and pure NB models. Keywords: logistic regression, na¨ıve Bayes, hybrid logistic regression-na¨ıve Bayes 1. Introduction Data classification is a very common task in machine learning and statistics. It is considered an in- stance of supervised learning. For a standard supervised learning problem, we implement a learning algorithm on a set of training examples, which are pairs consisting of an input object, a vector of explanatory variables F1;:::;Fn, called features, and a desired output object, a response variable C, called class variable.
    [Show full text]
  • Data-Driven Topo-Climatic Mapping with Machine Learning Methods
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by RERO DOC Digital Library Nat Hazards (2009) 50:497–518 DOI 10.1007/s11069-008-9339-y ORIGINAL PAPER Data-driven topo-climatic mapping with machine learning methods A. Pozdnoukhov Æ L. Foresti Æ M. Kanevski Received: 31 October 2007 / Accepted: 19 December 2008 / Published online: 16 January 2009 Ó Springer Science+Business Media B.V. 2009 Abstract Automatic environmental monitoring networks enforced by wireless commu- nication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influ- enced by the relief. The article shows how these relations, possibly regionalized and non- linear, can be modelled from data using the information from digital elevation models.
    [Show full text]
  • A Discriminative Model to Predict Comorbidities in ASD
    A Discriminative Model to Predict Comorbidities in ASD The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:38811457 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA i Contents Contents i List of Figures ii List of Tables iii 1 Introduction 1 1.1 Overview . 2 1.2 Motivation . 2 1.3 Related Work . 3 1.4 Contribution . 4 2 Basic Theory 6 2.1 Discriminative Classifiers . 6 2.2 Topic Modeling . 10 3 Experimental Design and Methods 13 3.1 Data Collection . 13 3.2 Exploratory Analysis . 17 3.3 Experimental Setup . 18 4 Results and Conclusion 23 4.1 Evaluation . 23 4.2 Limitations . 28 4.3 Future Work . 28 4.4 Conclusion . 29 Bibliography 30 ii List of Figures 3.1 User Zardoz's post on AutismWeb in March 2006. 15 3.2 User Zardoz's only other post on AutismWeb, published in February 2006. 15 3.3 Number of CUIs extracted per post. 17 3.4 Distribution of associated ages extracted from posts. 17 3.5 Number of posts written per author . 17 3.6 Experimental Setup . 22 4.1 Distribution of AUCs for single-concept word predictions . 25 4.2 Distribution of AUCs for single-concept word persistence . 25 4.3 AUC of single-concept word vs.
    [Show full text]
  • Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images
    ACTA INFOLOGICA dergipark.org.tr/acin DOI: 10.26650/acin.880918 RESEARCH ARTICLE Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images Beyin MR Görüntülerini Sınıflandırmada Farklı Önceden Eğitilmiş Derin Öğrenme Modellerinin Performans Karşılaştırması Onur Sevli1 ABSTRACT A brain tumor is a collection of abnormal cells formed as a result of uncontrolled cell division. If tumors are not diagnosed in a timely and accurate manner, they can cause fatal consequences. One of the commonly used techniques to detect brain tumors is magnetic resonance imaging (MRI). MRI provides easy detection of abnormalities in the brain with its high resolution. MR images have traditionally been studied and interpreted by radiologists. However, with the development of technology, it becomes more difficult to interpret large amounts of data produced in reasonable periods. Therefore, the development of computerized semi-automatic or automatic methods has become an important research topic. Machine learning methods that can predict by learning from data are widely used in this field. However, the extraction of image features requires special engineering in the machine learning process. Deep learning, a sub-branch of machine learning, allows us to automatically discover the complex hierarchy in the data and eliminates the limitations of machine learning. Transfer learning is to transfer the knowledge of a pre-trained neural network to a similar model in case of limited training data or the goal of reducing the workload. In this study, the performance of the pre-trained Vgg-16, ResNet50, Inception v3 models in classifying 253 brain MR images were evaluated. The Vgg-16 model showed the highest success with 94.42% accuracy, 83.86% recall, 100% precision and 91.22% F1 score.
    [Show full text]
  • A Latent Model of Discriminative Aspect
    A Latent Model of Discriminative Aspect Ali Farhadi 1, Mostafa Kamali Tabrizi 2, Ian Endres 1, David Forsyth 1 1 Computer Science Department University of Illinois at Urbana-Champaign {afarhad2,iendres2,daf}@uiuc.edu 2 Institute for Research in Fundamental Sciences [email protected] Abstract example, the camera location in the room might be a rough proxy for aspect. However, we believe this notion of aspect Recognition using appearance features is confounded by is not well suited for recognition tasks. First, it imposes phenomena that cause images of the same object to look dif- the unnatural constraint that objects in different classes be ferent, or images of different objects to look the same. This aligned into corresponding pairs of views. This is unnatural may occur because the same object looks different from dif- because the front of a car looks different to the front of a ferent viewing directions, or because two generally differ- bicycle. Worse, the appearance change from the side to the ent objects have views from which they look similar. In this front of a car is not comparable with that from side to front paper, we introduce the idea of discriminative aspect, a set of a bicycle. Second, the geometrical notion of aspect does of latent variables that encode these phenomena. Changes not identify changes that affect discrimination. We prefer a in view direction are one cause of changes in discrimina- notion of aspect that is explicitly discriminative, even at the tive aspect, but others include changes in texture or light- cost of geometric precision. ing.
    [Show full text]
  • On Surrogate Supervision Multi-View Learning
    AN ABSTRACT OF THE THESIS OF Gaole Jin for the degree of Master of Science in Electrical and Computer Engineering presented on December 3, 2012. Title: On Surrogate Supervision Multi-view Learning Abstract approved: Raviv Raich Data can be represented in multiple views. Traditional multi-view learning meth- ods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary view, although information from the target view is sufficient for learning task. However, this work addresses a semi-supervised case of multi-view learning, the surrogate supervision multi-view learning, where labels are available on limited views and a classifier is obtained on the target view where labels are missing. In surrogate multi-view learning, one cannot obtain a classifier without information from the auxiliary view. To solve this challenging problem, we propose discriminative and generative approaches. c Copyright by Gaole Jin December 3, 2012 All Rights Reserved On Surrogate Supervision Multi-view Learning by Gaole Jin A THESIS submitted to Oregon State University in partial fulfillment of the requirements for the degree of Master of Science Presented December 3, 2012 Commencement June 2013 Master of Science thesis of Gaole Jin presented on December 3, 2012. APPROVED: Major Professor, representing Electrical and Computer Engineering Director of the School of Electrical Engineering and Computer Science Dean of the Graduate School I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request. Gaole Jin, Author ACKNOWLEDGEMENTS My most sincere thanks go to my advisor, Dr.
    [Show full text]
  • Generative and Discriminative Models
    30 Jonathan Richard Shewchuk 6 Decision Theory; Generative and Discriminative Models DECISION THEORY aka Risk Minimization [Today I’m going to talk about a style of classifier very di↵erent from SVMs. The classifiers we’ll cover in the next few weeks are based on probability.] [One aspect of probabilistic data is that sometimes a point in feature space doesn’t have just one class. Suppose one borrower with income $30,000 and debt $15,000 defaults. another ” ” ” ” ” ” ” doesn’t default. So in your feature space, you have two feature vectors at the same point with di↵erent classes. Obviously, in that case, you can’t draw a decision boundary that classifies all points with 100% accuracy.] Multiple sample points with di↵erent classes could lie at same point: we want a probabilistic classifier. Suppose 10% of population has cancer, 90% doesn’t. Probability distributions for calorie intake, P(X Y): [caps here mean random variables, not matrices.] | calories (X) < 1,200 1,200–1,600 > 1,600 cancer (Y = 1) 20% 50% 30% no cancer (Y = 1) 1% 10% 89% − [I made these numbers up. Please don’t take them as medical advice.] Recall: P(X) = P(X Y = 1) P(Y = 1) + P(X Y = 1) P(Y = 1) | | − − P(1,200 X 1,600) = 0.5 0.1 + 0.1 0.9 = 0.14 ⇥ ⇥ You meet guy eating x = 1,400 calories/day. Guess whether he has cancer? [If you’re in a hurry, you might see that 50% of people with cancer eat 1,400 calories, but only 10% of people with no cancer do, and conclude that someone who eats 1,400 calories probably has cancer.
    [Show full text]
  • Adaptive Discriminative Generative Model and Its Applications
    Adaptive Discriminative Generative Model and Its Applications Ruei-Sung Lin† David Ross‡ Jongwoo Lim† Ming-Hsuan Yang∗ †University of Illinois ‡University of Toronto ∗Honda Research Institute [email protected] [email protected] [email protected] [email protected] Abstract This paper presents an adaptive discriminative generative model that gen- eralizes the conventional Fisher Linear Discriminant algorithm and ren- ders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best sep- arates the target from the background. We present a computationally efficient algorithm to constantly update this discriminative model as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative genera- tive model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. Nu- merous experiments show that our method is able to learn a discrimina- tive generative model for tracking target objects undergoing large pose and lighting changes. 1 Introduction Tracking moving objects is an important and essential component of visual perception, and has been an active research topic in computer vision community for decades. Object tracking can be formulated as a continuous state estimation problem where the unobserv- able states encode the locations or motion parameters of the target objects, and the task is to infer the unobservable states from the observed images over time. At each time step, a tracker first predicts a few possible locations (i.e., hypotheses) of the target in the next frame based on its prior and current knowledge.
    [Show full text]
  • Logistic Regression, Generative and Discriminative Classifiers
    Logistic Regression, Generative and Discriminative Classifiers Recommended reading: • Ng and Jordan paper “On Discriminative vs. Generative classifiers: A comparison of logistic regression and naïve Bayes,” A. Ng and M. Jordan, NIPS 2002. Machine Learning 10-701 Tom M. Mitchell Carnegie Mellon University Thanks to Ziv Bar-Joseph, Andrew Moore for some slides Overview Last lecture: • Naïve Bayes classifier • Number of parameters to estimate • Conditional independence This lecture: • Logistic regression • Generative and discriminative classifiers • (if time) Bias and variance in learning Generative vs. Discriminative Classifiers Training classifiers involves estimating f: X Æ Y, or P(Y|X) Generative classifiers: • Assume some functional form for P(X|Y), P(X) • Estimate parameters of P(X|Y), P(X) directly from training data • Use Bayes rule to calculate P(Y|X= xi) Discriminative classifiers: 1. Assume some functional form for P(Y|X) 2. Estimate parameters of P(Y|X) directly from training data • Consider learning f: X Æ Y, where • X is a vector of real-valued features, < X1 …Xn > • Y is boolean • So we use a Gaussian Naïve Bayes classifier • assume all Xi are conditionally independent given Y • model P(Xi | Y = yk) as Gaussian N(µik,σ) • model P(Y) as binomial (p) • What does that imply about the form of P(Y|X)? • Consider learning f: X Æ Y, where • X is a vector of real-valued features, < X1 …Xn > • Y is boolean • assume all Xi are conditionally independent given Y • model P(Xi | Y = yk) as Gaussian N(µik,σ) • model P(Y) as binomial (p)
    [Show full text]
  • Discriminative and Generative Models for Clinical Risk Estimation: an Empirical Comparison
    Discriminative and Generative Models for Clinical Risk Estimation: an Empirical Comparison John Stamford1 and Chandra Kambhampati2 1 Department of Computer Science, The University of Hull, Kingston upon Hull, HU6 7RX, United Kingdom [email protected] 2 Department of Computer Science, The University of Hull, Kingston upon Hull, HU6 7RX, United Kingdom [email protected] Abstract Linear discriminative models, in the form of Logistic Regression, are a popular choice within the clinical domain in the development of risk models. Logistic regression is commonly used as it offers explanatory information in addition to its predictive capabilities. In some examples the coefficients from these models have been used to determine overly simplified clinical risk scores. Such models are constrained to modeling linear relationships between the variables and the class despite it known that this relationship is not always linear. This paper compares the conditions under which linear discriminative and linear generative models perform best. This is done through comparing logistic regression and naïve Bayes on real clinical data. The work shows that generative models perform best when the internal representation of the data is closer to the true distribution of the data and when there is a very small difference between the means of the classes. When looking at variables such as sodium it is shown that logistic regression can not model the observed risk as it is non-linear in its nature, whereas naïve Bayes gives a better estimation of risk. The work concludes that the risk estimations derived from discriminative models such as logistic regression need to be considered in the wider context of the true risk observed within the dataset.
    [Show full text]
  • Joint Classification of Actions and Object State Changes with a Latent Variable Discriminative Model
    Joint classification of actions and object state changes with a latent variable discriminative model Efstathios Vafeias1 and Subramanian Ramamoorthy2 Abstract— We present a technique to classify human actions with the environment that change the context of the action. that involve object manipulation. Our focus is to accurately In this paper, our focus is on trying to model this notion distinguish between actions that are related in that the object’s of context as well. Unlike methods that are based, say, on state changes define the essential differences. Our algorithm uses a latent variable conditional random field that allows features of single frames within the activity, we model spatio- for the modelling of spatio-temporal relationships between the temporal interactions with objects that define the classifica- human motion and the corresponding object state changes. tion outcome of an action. This makes our method suitable Our approach involves a factored representation that better for the understanding of activities which are best defined by allows for the description of causal effects in the way human the joint evolution of the state of an object in the environment action causes object state changes. The utility of incorporating such structure in our model is that it enables more accurate and the changes in body poses that cause that state to change. classification of activities that could enable robots to reason We present a technique that learns to classify such inter- about interaction, and to learn using a high level vocabulary actions, from video data, and performs better than alternate that captures phenomena of interest. We present experiments baselines due to its use of the joint information in the involving the recognition of human actions, where we show that recognition process.
    [Show full text]