scholarly journals Classification Performance of Answer-Copying Indices Under Different Types of IRT Models

2016 ◽  
Vol 40 (8) ◽  
pp. 592-607 ◽  
Author(s):  
Cengiz Zopluoglu
Keyword(s):  
Classification Performance ◽  
Irt Models ◽  
Different Types ◽  
Answer Copying

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

scholarly journals Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 251 ◽  
Author(s):  
Wael Ghada ◽  
Nicole Estrella ◽  
Annette Menzel
Keyword(s):  
Machine Learning ◽  
Geographical Location ◽  
Classification Performance ◽  
Classification Methods ◽  
Reference Dataset ◽  
Machine Learning Classification ◽  
Machine Learning Approach ◽  
Different Types ◽  
Microstructure Parameters

Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity.

scholarly journals Aircraft Target Classification Method for Conventional Narrowband Radar Based on Micro-Doppler Effect

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Saiqiang Xia ◽  
Chaowei Zhang ◽  
Wanyong Cai ◽  
Jun Yang ◽  
Liangfa Hua ◽  
...  
Keyword(s):  
Doppler Effect ◽  
Classification Problem ◽  
Classification Performance ◽  
Central Moment ◽  
Classification Method ◽  
Support Vector ◽  
Machine Model ◽  
Different Types ◽  
Characteristics Analysis ◽  
The Time Domain

For a conventional narrowband radar system, its insufficient bandwidth usually leads to the lack of detectable information of the target, and it is difficult for the radar to classify the target types, such as rotor helicopter, propeller aircraft, and jet aircraft. To address the classification problem of three different types of aircraft target, a joint multifeature classification method based on the micro-Doppler effect in the echo caused by the target micromotion is proposed in this paper. Through the characteristics analysis of the target simulation echoes obtained from the target scattering point model, four features with obvious distinguishability are extracted from the time domain and frequency domain, respectively, that is, flicker interval, fractal dimension, modulation bandwidth, and second central moment. Then, a support vector machine model will be applied to the classification of the three different types of aircraft. Compared with the conventional method, the proposed method has better classification performance and can significantly improve the classification probability of aircraft target. The simulations are carried out to validate the effectiveness of the proposed method.

scholarly journals A Theoretical Analysis of Why Hybrid Ensembles Work

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Kuo-Wei Hsu
Keyword(s):  
Theoretical Analysis ◽  
Theoretical Foundation ◽  
Group Decision ◽  
Classification Performance ◽  
Decision Making Process ◽  
Classification Algorithms ◽  
Data Mining Algorithm ◽  
Ensembles Of Classifiers ◽  
Mining Algorithm ◽  
Different Types

Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

scholarly journals A comprehensive comparison on evolutionary feature selection approaches to classification

2021 ◽  
Author(s):  
Bing Xue ◽  
Mengjie Zhang ◽  
William Browne
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Decision Makers ◽  
Computational Time ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
Comprehensive Comparison ◽  
Single Objective

© 2015 Imperial College Press. Feature selection is an important data preprocessing step in machine learning and data mining, such as classification tasks. Research on feature selection has been extensively conducted for more than 50 years and different types of approaches have been proposed, which include wrapper approaches or filter approaches, and single objective approaches or multi-objective approaches. However, the advantages and disadvantages of such approaches have not been thoroughly investigated. This paper provides a comprehensive study on comparing different types of feature selection approaches, specifically including comparisons on the classification performance and computational time of wrappers and filters, generality of wrapper approaches, and comparisons on single objective and multi-objective approaches. Particle swarm optimization (PSO)-based approaches, which include different types of methods, are used as typical examples to conduct this research. A total of 10 different feature selection methods and over 7000 experiments are involved. The results show that filters are usually faster than wrappers, but wrappers using a simple classification algorithm can be faster than filters. Wrappers often achieve better classification performance than filters. Feature subsets obtained from wrappers can be general to other classification algorithms. Meanwhile, multi-objective approaches are generally better choices than single objective algorithms. The findings are not only useful for researchers to develop new approaches to addressing new challenges in feature selection, but also useful for real-world decision makers to choose a specific feature selection method according to their own requirements.

scholarly journals Frisky CALF outruns LASSO

2020 ◽  
Author(s):  
CD Jeffries ◽  
JR Ford ◽  
JL Tilson ◽  
DO Perkins ◽  
D Bost ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Blood Plasma ◽  
Plasma Proteins ◽  
Input Data ◽  
Classification Performance ◽  
Body Of Knowledge ◽  
Different Types ◽  
Blood Plasma Proteins ◽  
Reliable Interpretation

AbstractRegression analysis is a mature body of knowledge, but there might be room for one more strategy with advantages regarding a class of problems common in modern medical research. Specifically, modern lines of investigation often involve several tens or a few hundred subjects but several hundred assays of cryptically related markers (e.g. blood plasma proteins for ~100 patients vs the same for ~100 unaffected persons). The class arises because research is expensive and sometimes carries a degree of risk to subjects. Also, reliable interpretation of results may employ not just a set of individually distinguished markers, but networks of related markers that are collectively—but not so much individually—informative. This article explores a linear regression strategy that chooses markers parsimoniously but also calculates weights robustly in the sense that both marker choices and weights are generally invariant with respect to small changes in input data. Furthermore, the method allows integration of markers of very different types, thereby improving classification performance and suggesting etiologies and treatments.

scholarly journals A comprehensive comparison on evolutionary feature selection approaches to classification

2021 ◽  
Author(s):  
Bing Xue ◽  
Mengjie Zhang ◽  
William Browne
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Decision Makers ◽  
Computational Time ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
Comprehensive Comparison ◽  
Single Objective

© 2015 Imperial College Press. Feature selection is an important data preprocessing step in machine learning and data mining, such as classification tasks. Research on feature selection has been extensively conducted for more than 50 years and different types of approaches have been proposed, which include wrapper approaches or filter approaches, and single objective approaches or multi-objective approaches. However, the advantages and disadvantages of such approaches have not been thoroughly investigated. This paper provides a comprehensive study on comparing different types of feature selection approaches, specifically including comparisons on the classification performance and computational time of wrappers and filters, generality of wrapper approaches, and comparisons on single objective and multi-objective approaches. Particle swarm optimization (PSO)-based approaches, which include different types of methods, are used as typical examples to conduct this research. A total of 10 different feature selection methods and over 7000 experiments are involved. The results show that filters are usually faster than wrappers, but wrappers using a simple classification algorithm can be faster than filters. Wrappers often achieve better classification performance than filters. Feature subsets obtained from wrappers can be general to other classification algorithms. Meanwhile, multi-objective approaches are generally better choices than single objective algorithms. The findings are not only useful for researchers to develop new approaches to addressing new challenges in feature selection, but also useful for real-world decision makers to choose a specific feature selection method according to their own requirements.

scholarly journals pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1529
Author(s):  
Muhammad Shujaat ◽  
Abdul Wahab ◽  
Hilal Tayara ◽  
Kil To Chong
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Small Region ◽  
Classification Performance ◽  
Specific Gene ◽  
Computational Tools ◽  
Encoding Scheme ◽  
Different Types ◽  
Important Challenge

A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools.

scholarly journals Classification Performance Using Principal Component Analysis and Different Value of the Ratio R

2011 ◽  
Vol 6 (2) ◽  
pp. 317 ◽  
Author(s):  
Jasmina Novakovic ◽  
Sinisa Rankov
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Data Sets ◽  
Best Value ◽  
Inherent Feature ◽  
Different Types

A comparison between several classification algorithms with feature extraction on real dataset is presented. Principal Component Analysis (PCA) has been used for feature extraction with different values of the ratio R, evaluated and compared using four different types of classifiers on two real benchmark data sets. Accuracy of the classifiers is influenced by the choice of different values of the ratio R. There is no best value of the ratio R, for different datasets and different classifiers accuracy curves as a function of the number of features used may significantly differ. In our cases feature extraction is especially effective for classification algorithms that do not have any inherent feature selections or feature extraction build in, such as the nearest neighbour methods or some types of neural networks.

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