scholarly journals Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 429
Author(s):  
Marko Horvat ◽  
Alan Jović ◽  
Danko Ivošević
Keyword(s):  
Binary Classification ◽  
Complete Information ◽  
Document Retrieval ◽  
International Affective Picture System ◽  
Multimedia Databases ◽  
Matching Method ◽  
Approximate Matching ◽  
Domain Optimization ◽  
Binary Classifiers ◽  
Typical Scenario

Evaluation of document classification is straightforward if complete information on the documents’ true categories exists. In this case, the rank of each document can be accurately determined and evaluated. However, in an unsupervised setting, where the exact document category is not available, lift charts become an advantageous method for evaluation of the retrieval quality and categorization of ranked documents. We introduce lift charts as binary classifiers of ranked documents and explain how to apply them to the concept-based retrieval of emotionally annotated images as one of the possible retrieval methods for this application. Furthermore, we describe affective multimedia databases on a representative example of the International Affective Picture System (IAPS) dataset, their applications, advantages, and deficiencies, and explain how lift charts may be used as a helpful method for document retrieval in this domain. Optimization of lift charts for recall and precision is also described. A typical scenario of document retrieval is presented on a set of 800 affective pictures labeled with an unsupervised glossary. In the lift charts-based retrieval using the approximate matching method, the highest attained accuracy, precision, and recall were 51.06%, 47.41%, 95.89%, and 81.83%, 99.70%, 33.56%, when optimized for recall and precision, respectively.

MINIMAL CLASSIFICATION METHOD WITH ERROR-CORRECTING CODES FOR MULTICLASS RECOGNITION

2005 ◽  
Vol 19 (05) ◽  
pp. 663-680 ◽  
Author(s):  
DAYAN MANOHAR SIVALINGAM ◽  
NARENKUMAR PANDIAN ◽  
JEZEKIEL BEN-ARIE
Keyword(s):  
Binary Classification ◽  
Classification Problem ◽  
Error Correcting Codes ◽  
Classification Method ◽  
Support Vector ◽  
Object Image ◽  
Generalization Error ◽  
Image Library ◽  
Binary Classification Problem ◽  
Binary Classifiers

In this work, we develop an efficient technique to transform a multiclass recognition problem into a minimal binary classification problem using the Minimal Classification Method (MCM). The MCM requires only log 2 N classifications whereas the other methods require much more. For the classification, we use Support Vector Machine (SVM) based binary classifiers since they have superior generalization performance. Unlike the prevalent one-versus-one strategy (the bottom-up one-versus-one strategy is called tournament method) that separates only two classes at each classification, the binary classifiers in our method have to separate two groups of multiple classes. As a result, the probability of generalization error increases. This problem is alleviated by utilizing error correcting codes, which results only in a marginal increase in the required number of classifications. However, in comparison to the tournament method, our method requires only 50% of the classifications and still similar performance can be attained. The proposed solution is tested with the Columbia Object Image Library (COIL). We also test the performance under conditions of noise and occlusion.

scholarly journals Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1450
Author(s):  
Seul-Gi Kim ◽  
Donghyun Park ◽  
Jae-Yoon Jung
Keyword(s):  
Fault Detection ◽  
Binary Classification ◽  
Rotating Machinery ◽  
Industrial Robots ◽  
Sensor Data ◽  
Support Vector ◽  
Imbalanced Classification ◽  
Vibration Data ◽  
Binary Classifiers ◽  
One Class Classification

Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field.

scholarly journals Designing a Document Retrieval Method for University Digital Libraries Based on Hadoop Technology

2021 ◽  
Vol 5 (12) ◽  
pp. 82-87
Author(s):  
Haixia He
Keyword(s):  
Big Data ◽  
Semantic Similarity ◽  
Digital Libraries ◽  
Document Retrieval ◽  
University Libraries ◽  
Matching Method ◽  
Retrieval Method ◽  
Calculation Process

With the development of big data, all walks of life in society have begun to venture into big data to serve their own enterprises and departments. Big data has been embraced by university digital libraries. The most cumbersome work for the management of university libraries is document retrieval. This article uses Hadoop algorithm to extract semantic keywords and then calculates semantic similarity based on the literature retrieval keyword calculation process. The fast-matching method is used to determine the weight of each keyword, so as to ensure an efficient and accurate document retrieval in digital libraries, thus completing the design of the document retrieval method for university digital libraries based on Hadoop technology.

Boosting the classification performance of latent fingerprint segmentation using cascade of classifiers

2020 ◽  
Vol 14 (3) ◽  
pp. 359-371 ◽  
Author(s):  
Megha Chhabra ◽  
Manoj Kumar Shukla ◽  
Kiran Kumar Ravulakollu
Keyword(s):  
Binary Classification ◽  
Poor Quality ◽  
Classification Performance ◽  
Latent Fingerprints ◽  
Fingerprint Segmentation ◽  
Binary Classifiers ◽  
Maximal Independent Sets ◽  
Optimal Set ◽  
Optimal Feature

Segmentation and classification of latent fingerprints is a young challenging area of research. Latent fingerprints are unintentional fingermarks. These marks are ridge patterns left at crime scenes, lifted with latent or unclear view of fingermarks, making it difficult to find the guilty party. The segmentation of lifted images of such finger impressions comes with some unique challenges in domain such as poor quality images, incomplete ridge patterns, overlapping prints etc. The classification of poorly acquired data can be improved with image pre-processing, feeding all or optimal set of features extracted to suitable classifiers etc. Our classification system proposes two main steps. First, various effective extracted features are compartmentalised into maximal independent sets with high correlation value, Second, conventional supervised technique based binary classifiers are combined into a cascade/stack of classifiers. These classifiers are fed with all or optimal feature set(s) for binary classification of fingermarks as ridge patterns from non-ridge background. The experimentation shows improvement in accuracy rate on IIIT-D database with supervised algorithms.

scholarly journals Automobile Predictive Maintenance using Deep Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 0-0
Keyword(s):  
Preventive Maintenance ◽  
Binary Classification ◽  
Decision Rules ◽  
Complete Information ◽  
Predictive Maintenance ◽  
Maintenance Management ◽  
Classification Model ◽  
Management Policy ◽  
Maintenance Cycle ◽  
Maintenance Schedules

There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

scholarly journals Multi-Class Support Vector Machine via Maximizing Multi-Class Margins

2017 ◽  
Author(s):  
Jie Xu ◽  
Xianglong Liu ◽  
Zhouyuan Huo ◽  
Cheng Deng ◽  
Feiping Nie ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Training Data ◽  
Classification Model ◽  
Support Vector ◽  
Frobenius Norm ◽  
Great Success ◽  
Svm Model ◽  
Binary Classifiers ◽  
And Training

Support Vector Machine (SVM) is originally proposed as a binary classification model, and it has already achieved great success in different applications. In reality, it is more often to solve a problem which has more than two classes. So, it is natural to extend SVM to a multi-class classifier. There have been many works proposed to construct a multi-class classifier based on binary SVM, such as one versus all strategy, one versus one strategy and Weston's multi-class SVM. One versus all strategy and one versus one strategy split the multi-class problem to multiple binary classification subproblems, and we need to train multiple binary classifiers. Weston's multi-class SVM is formed by ensuring risk constraints and imposing a specific regularization, like Frobenius norm. It is not derived by maximizing the margin between hyperplane and training data which is the motivation in SVM. In this paper, we propose a multi-class SVM model from the perspective of maximizing margin between training points and hyperplane, and analyze the relation between our model and other related methods. In the experiment, it shows that our model can get better or compared results when comparing with other related methods.

scholarly journals Teacher Moments: A digital clinical simulation platform with extensible AI architecture

2021 ◽  
Author(s):  
Garron Hillaire ◽  
Rick Waldron ◽  
Chas Murray ◽  
Ritam Dutt ◽  
Gabrielle R Marvez ◽  
...  
Keyword(s):  
Police Officers ◽  
Binary Classification ◽  
Training Data ◽  
Conversational Agents ◽  
Dynamic Learning ◽  
Dynamic Display ◽  
Audio Data ◽  
Single User ◽  
Binary Classifiers ◽  
Display Content

Teacher Moments is an open source platform that allows the authoring of simulations used for education which we recently revised to integrate intelligent coaching agents. The initial simulation development for Teacher Moments focused on teacher education, but the platform is actively used for professional development with nurses, psychologists, police officers, judges, and attorneys. Simulations can range in complexity from single-user simulations to multi-user role-play simulations. Single-user simulations provide opportunities for participants to respond using text or audio inputs while multiuser simulations extend those response types to include chat functionality. To support participant learning, Teacher Moments simulations can now be configured to include intelligent coaching agents that review participant inputs, identify salient patterns in text or speech, and respond with feedback and coaching supports. Teacher Moments can be configured to incorporate text or audio binary classifiers or include conversational agents into the chat feature. Once a classifier is configured there is functionality to dynamically display content based on audio or text classification when authoring the simulation. In addition, conversational agents can interject comments into the chat directed at either a particular participant or to all participants in a chat. Finally, there is a new integrated labeling component that supports collecting binary labels from participants for text or audio data, which can be used either to validate the accuracy of a classifier or to establish training data for a classifier. In this demo, we will: 1) highlight GitHub repositories designed to support the deployment of classifiers that can be integrated into Teacher Moments; 2) demonstrate a conversational agent integrated into the chat feature to provide intelligent supports; 3) illustrate how binary classification can trigger the dynamic display of content providing options for dynamic learning supports; and 4) demonstrate how the labeling component can be used for either validation of a classifier or collection of training data.

scholarly journals A Robust Machine Learning Based Framework for the Automated Detection of ADHD Using Pupillometric Biomarkers and Time Series Analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
William Das ◽  
Shubh Khanna
Keyword(s):  
Machine Learning ◽  
Pupil Size ◽  
Binary Classification ◽  
Approximate Entropy ◽  
Automated Detection ◽  
Support Vector ◽  
Efficient Detection ◽  
Absolute Energy ◽  
Binary Classifiers ◽  
Diagnostic Applications

AbstractAccurate and efficient detection of attention-deficit/hyperactivity disorder (ADHD) is critical to ensure proper treatment for affected individuals. Current clinical examinations, however, are inefficient and prone to misdiagnosis, as they rely on qualitative observations of perceived behavior. We propose a robust machine learning based framework that analyzes pupil-size dynamics as an objective biomarker for the automated detection of ADHD. Our framework integrates a comprehensive pupillometric feature engineering and visualization pipeline with state-of-the-art binary classification algorithms and univariate feature selection. The support vector machine classifier achieved an average 85.6% area under the receiver operating characteristic (AUROC), 77.3% sensitivity, and 75.3% specificity using ten-fold nested cross-validation (CV) on a declassified dataset of 50 patients. 218 of the 783 engineered features, including fourier transform metrics, absolute energy, consecutive quantile changes, approximate entropy, aggregated linear trends, as well as pupil-size dilation velocity, were found to be statistically significant differentiators (p < 0.05), and provide novel behavioral insights into associations between pupil-size dynamics and the presence of ADHD. Despite a limited sample size, the strong AUROC values highlight the robustness of the binary classifiers in detecting ADHD—as such, with additional data, sensitivity and specificity metrics can be substantially augmented. This study is the first to apply machine learning based methods for the detection of ADHD using solely pupillometrics, and highlights its strength as a potential discriminative biomarker, paving the path for the development of novel diagnostic applications to aid in the detection of ADHD using oculometric paradigms and machine learning.

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