Improved Naive Bayes for better phone call behavior classification.
problem Noise in mobile phone data affects phone call behavior classification accuracy.
method Improved naive Bayes classifier with behavioral pattern analysis and dynamic noise threshold.
result Our technique improves classification accuracy by 15%.
Interactive tool helps analyze classifier prediction scores.
problem Understanding and assessing the reliability of multi-class classifiers.
method Interactive visualization tool (Classilist) to analyze prediction scores.
result Varying classifier behavior revealed through analysis of prediction scores.
We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. …
Paper models MOOC users behavior using MDP for easier classification.
problem Difficulty in defining and classifying MOOC users based on their behavior.
method Uses Markov Decision Process (MDP) to model user behavior from log data.
result Classifies users based on their intentions inferred from MDP rewards.
Novel algorithm improves poultry welfare by classifying chicken behaviors.
problem Classifying chicken behaviors from sparse and noisy data.
method Time series dictionary learning algorithm.
result Robustly learns from weakly labeled data sources.
Study examines how classifier performance is affected by training data quality.
problem How classifier performance is affected by training data quality.
method Extensive numerical experiments with four classifiers (Bayes, neural nets, partition models, random forests) on metagenomic assembly data.
result Classifier performance degrades as training data quality degrades, leading to breakdown-like behavior.
The paper proposes sampling strategies for classifier copies.
problem Generating unlabelled points to explore decision behavior.
method Two sampling strategies compared with two standard methods.
result Validation in six problems and comparison of performance and cost.
A new deep learning method classifies VM behavior in cloud systems.
problem Scalability issues in monitoring and managing cloud data centers.
method Deep learning classifiers (DeepConv and DeepFFT) for VM behavior identification.
result Our method achieves better performance and is significantly faster.
Captures user behaviors in evolving time series data.
problem Modeling time series data with evolving patterns.
method Defines evolution genes, learns classifiers, and estimates segment distributions.
result Achieves better prediction results and provides explanations.
The paper develops classifiers that encourage positive adaptation in machine learning settings.
problem Strategic behavior by decision subjects leads to performance loss in machine learning models.
method Formulates a two-stage game to characterize optimal strategies for model designers and decision subjects.
result Trained classifiers maintain accuracy while inducing higher improvement and less manipulation.
Paper builds ML classifier to detect crypto-ransomware.
problem Detecting crypto-ransomware with high accuracy and low false positives.
method Behavior-based detection using input/output activities and file-content entropy. Deep-learning classifier with adversarial research and Integrated Gradient method for explanation.
result Deep-learning classifier achieves high accuracy and low false positive rate in detecting crypto-ransomware.
This paper provides new insight into maximizing F1 scores in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, F1 score is widely used to measure the success of a binary classifier when one class is rare. Micro average, macro average, a…
The support vector machine (SVM) is a powerful and widely used classification algorithm. This paper uses the Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof for new insights into the behavior of SVM. These insights provide perhaps unexpected relationships between SVM and two other linear classifier…
Detects student engagement states using unobtrusive appearance, context, and mouse data.
problem Detecting student engagement states in a classroom setting.
method Multimodal approach combining appearance, context, and mouse data; classifiers are fused at the decision level.
result Effective detection of student engagement states in a classroom setting.
Paper proposes a beta distribution method for detecting concept drift in adaptive classifiers.
problem Adaptive classifiers need to detect and respond to concept drift in real-time data streams.
method The paper introduces a beta distribution model to monitor model error and identify abnormal behavior as drift.
result The method effectively detects abrupt changes in model error, improving classifier performance.
The paper classifies solitons for a specific type of flow.
problem Classifying solitons for a fully non-linear Yamabe flow.
method Careful analysis of an associated dynamical system.
result Existence and description of solitons for certain dimensions.
New framework shows strategic behavior is actually a form of causal modeling.
problem Designing classifiers that incentivize strategic behavior to improve quality.
method Developed a causal framework to distinguish between gaming and improvement.
result Proved any procedure for designing incentive classifiers must solve a causal inference problem.
Deep learning classifies animal behavior from wearable accelerometers.
problem Classifying animal behavior from accelerometer data.
method End-to-end deep neural network with IIR and FIR filters.
result Outperforms state-of-the-art algorithms in real-time classification.
Most animals possess the ability to actuate a vast diversity of movements, ostensibly constrained only by morphology and physics. In practice, however, a frequent assumption in behavioral science is that most of an animal's activities can be described in terms of a small set of stereotyped motifs. Here we introduce a m…
Study classifies positive solutions to a biharmonic equation near singularities.
problem Classifying positive solutions to a nonlinear biharmonic equation with critical exponent.
method Analyzes periodic functions of the logarithm of the distance to the origin.
result Identifies periodic functions of the logarithm of the distance to the origin as solutions.
Study explores cyberbullying datasets and classifier generalization.
problem Cyberbullying detection across different online communities.
method Analysis of various cyberbullying datasets and classifier generalization.
result Insight into classifier generalization power and interaction.
Survey visual analytics methods for detecting anomalous user behaviors.
problem Understanding and detecting anomalous user behaviors in various domains.
method Survey and classification of visual analytics methods in four categories.
result Discussion of findings and potential research directions.
Models show strategic agents can influence classifier outcomes by investing effort.
problem How strategic agents can influence classifier outcomes.
method Developed a model to characterize strategic effort investment.
result Simple linear mechanisms can incentivize strategic effort effectively.
Analyzes how learning algorithms affect and are affected by data manipulation.
problem Characterizing the closed-loop behavior of learning algorithms in the presence of decision-dependent data.
method Analyzes repeated risk minimization as perturbed gradient flows of performative risk minimization, considering multiple local minimizers.
result Characterizes the region of attraction for various equilibria and introduces performative alignment.
Paper targets clean-label poisoning attacks on neural nets.
problem Manipulating neural net behavior with poisoned data.
method Optimization-based and watermarking strategies for crafting and deploying poisons.
result A single poisoned image can control classifier behavior.
Study of linear classifiers in infinite imbalance scenarios.
problem Behavior of linear discriminant functions in extreme imbalance conditions.
method Analysis of linear classifiers under infinite imbalance, focusing on weight function properties and limit behavior.
result Limiting coefficient vectors reflect robustness or conservatism, optimizing against worst-case alternatives.
xGEMs explain black-box models by exploring data manifolds.
problem Understanding the behavior of black-box classifiers.
method Train an unsupervised generative model to represent the data manifold and perturb data points to analyze model behavior.
result Detect and quantify bias in model learning and changes in model behavior over training.
Paper uses machine learning to model travel mode switching under a new transit system.
problem Modeling individual travel mode preferences and response to new mobility options.
method Interpretable machine learning approach to predict and interpret mode-switching behavior.
result Machine learning captures individual heterogeneity in travel mode choice.
The paper proposes a method to trim Bayesian network classifiers robustly.
problem Removing costly features from Bayesian network classifiers while maintaining robustness.
method Introduces an expected classification agreement (ECA) metric and a branch-and-bound search algorithm to find optimal feature subsets and thresholds.
result The proposed method maximizes expected agreement between the original and trimmed classifiers, subject to a budgetary constraint.
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give near-optimal accuracy when trained on all data, while also minimizing the cost of misallocated samples. This is motivated…
Neural networks classify OOD images by their nearest neighbor in training data.
problem Understanding out-of-distribution prediction behavior of neural networks.
method Nearest category generalization (NCG) measure to assess OOD prediction accuracy.
result Adversarially robust networks have higher NCG accuracy than natural training, indicating local regularization impacts decision regions.
Improved average distance classifier for HDLSS settings with multiple population differences.
problem Poor performance of average distance classifier in HDLSS settings with location and scale differences.
method Proposed transformations to the average distance classifier to handle multiple population differences.
result The proposed classifiers perform well even when populations differ in other aspects than location and scale.
Stochastic defense improves natural classifiers against adversarial attacks.
problem Vulnerability of deep networks to adversarial attacks.
method Long-run MCMC sampling with Energy-Based Model for adversarial purification.
result Balancing memoryless and metastable behavior leads to effective purification and robust classification.
Classifies 2-uniform maps on torus with formulas and asymptotic bounds.
problem Classifying 2-uniform maps on torus.
method Classification through arithmetic functions and asymptotic analysis.
result Explicit formulas and asymptotic bounds for 2-uniform maps on torus.
Deep learning model predicts online fraud using customer behavior data.
problem Predicting online financial fraud from customer behavior data.
method Recurrent Neural Network (RNN) integrated with Markov Transition Field (MTF).
result The proposed model significantly improves fraud prediction compared to traditional methods.
This paper classifies chiral graphs up to size 12.
problem Understanding the chirality of simple graphs to predict molecular behavior.
method Classifying minor minimal intrinsically chiral graphs among simple graphs of size up to 12.
result Complete set of minor minimal graphs for intrinsic properties of chiral molecules.
Bistable structures associated with non-linear deformation behavior, exemplified by the Venus flytrap and slap bracelet, can switch between different functional shapes upon actuation. Despite numerous efforts in modeling such large deformation behavior of shells, the roles of mechanical and nonlinear geometric effects …
We propose a method to visualize class similarity in large-scale classifiers.
problem Analyzing hierarchical structures and relationships in large-scale classification.
method Compute class similarity based on prediction scores and visualize the class similarity matrix.
result Visualizing class similarity matrices reveals hierarchical structures and relationships.
A new framework for mobile authentication using deep metric learning.
problem Challenges in mobile authentication using behavioral biometrics.
method Deep metric learning, private data protection, flexible training scheduling.
result 95% authentication accuracy on public datasets, robust against attacks.
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
Study examines if LLMs' trading styles match real market behavior.
problem Lack of behavioral consistency in LLMs' trading strategies.
method Year-long simulations with LLMs, operationalizing behavioral finance drivers, and comparing with financial theory.
result LLMs' strategy switching is only partially consistent with behavioral finance theories.
This study improves detection of insider threats using machine learning.
problem Detecting insider threats with anticipation and accuracy.
method Empirically tested 88 machine learning algorithms on CERT dataset, focusing on employee sentiment.
result Random forest algorithms offer the best overall performance, with high accuracy and interpretable rules.
We classify 3-braids up to (2,2)-move equivalence and in particular we show how to adjust the Harikae-Nakanishi-Uchida conjecture so it holds for closed 3-braids. As important steps to classify 3-braids up to (2,2)-move equivalence we prove the conjecture for 2-algebraic links and classify (2,2)-equivalence classes for…
We study the behavior of Legendrian and transverse knots under the operation of connected sums. As a consequence we show that there exist Legendrian knots that are not distinguished by any known invariant. Moreover, we classify Legendrian knots in some non-Legendrian simple knot types.
New method shows unfair explanations can be created for any classifier.
problem Fairness in black-box classifier explanations is not guaranteed.
method Constructs a new classifier with similar performance but altered explanations.
result Explanation methods can be manipulated to show unfair decisions.
Modeling and learning turn-taking behaviors in multi-agent systems.
problem Modeling and predicting turn-taking behaviors in dynamic multi-agent systems.
method Individual behavior models (WFSTs) and multi-agent fusion model (logistic regression classifier).
result Accurately models and predicts turn-taking behaviors with high precision.
System detects multiple patients' behaviors in real-time using mmWave radar and CNN.
problem Real-time patient behavior monitoring in hospitals.
method Used mmWave radar for tracking and collecting Doppler patterns. Created a three-layer CNN model for behavior classification.
result System achieved very good inference accuracy in predicting patient behaviors in real-time.
Consumer Demand Response (DR) is an important research and industry problem, which seeks to categorize, predict and modify consumer's energy consumption. Unfortunately, traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it diff…