Analyzing Qualitative Predictors with Too Few Data: An Alternative Approach to Handling Sparse-Cell Values

1981 ◽  
Vol 18 (1) ◽  
pp. 63-72 ◽  
Author(s):  
William R. Dillon ◽  
Matthew Goldstein ◽  
Lucy Lement
Keyword(s):  
Marketing Manager ◽  
Store Choice ◽  
Data Set ◽  
New Approach ◽  
Alternative Approach ◽  
Modeling Techniques ◽  
Marketing Data ◽  
Few Data ◽  
Alternative Remedies

The marketing manager faces several dilemmas when analyzing multivariate frequency data. If the choice is to analyze a series of two-dimensional condensed tables, the interrelationships between those factors not in the table will be lost and biased inferences can result. If the decision is to analyze the complete multiway table, many of the cells may be sparse. The authors address the issue of how best to handle sparse-cell values in the context of a marketing data set relating store choice behavior to a number of shopper-specific variables. A simple new approach to this problem, which utilizes loglinear modeling techniques, is developed and contrasted with alternative remedies. The results of the comparative analysis show the proposed approach performs well, especially in the correct classification of seemingly unclassifiable shoppers.

scholarly journals Density-based classification with the DENCLUE algorithm

2021 ◽  
Vol 24 (1) ◽  
pp. 269
Author(s):  
Mouhcine El Hassani ◽  
Noureddine Falih ◽  
Belaid Bouikhalene
Keyword(s):  
Spatial Clustering ◽  
Data Partitioning ◽  
Clustering Methods ◽  
Data Set ◽  
Density Based Clustering ◽  
Marketing Data ◽  
Numerous Data ◽  
Bank Marketing ◽  
Probabilistic Nature

<p><span>Classification of information is a vague and difficult to explore area of research, hence the emergence of grouping techniques, often referred to Clustering. It is necessary to differentiate between an unsupervised and a supervised classification. Clustering methods are numerous. Data partitioning and hierarchization push to use them in parametric form or not. Also, their use is influenced by algorithms of a probabilistic nature during the partitioning of data. The choice of a method depends on the result of the Clustering that we want to have. This work focuses on classification using the density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithm through an application made in csharp. Through the use of three databases which are the IRIS database, breast cancer wisconsin (diagnostic) data set and bank marketing data set, we show experimentally that the choice of the initial data parameters is important to accelerate the processing and can minimize the number of iterations to reduce the execution time of the application.</span></p>

scholarly journals NEW APPROACH OF CLASSIFICATION OF ROLLING ELEMENT BEARING FAULT USING ARTIFICIAL NEURAL NETWORK

1970 ◽  
Vol 40 (2) ◽  
pp. 119-130 ◽  
Author(s):  
V. Hariharan ◽  
PSS. Srinivasan
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Data Set ◽  
New Approach ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing

The paper presents a new approach to the classification of rolling element bearing faults by implementing Artificial Neural Network. Diagnostics of rolling element bearing faults actually represents the problem of pattern classification and recognition, where the key step is feature extraction from the vibration signal. Characterization of each recorded vibration signal is performed by a combination of signal's time-varying statistical parameters and characteristic rolling element bearing fault frequency components obtained through the frequency spectrum analysis method. The experimental data is collected for four bearings at three different speeds. The sensor is located at three different positions for each bearing. Both time domain and frequency domain signals were measured. Thus the data was three time spectrums and three frequency spectrums for each speed for a bearing. The entire data set comprised of 72 (6 x 3 x 4) data. The time domain signal was comprised of 8192 samples and extracting these features from a huge data set was difficult. To overcome this difficulty the 8192 samples were split into 32 bins each containing 256 samples. Two Network RBFN and PNN are used to classify the bearing defects. The entire process of splitting and evaluating the seven features was coded in MATLAB.  From these seven features the most suitable features are for explaining the intensity of the defect is discussed.Key Words: Feature Extraction; Fault Frequencies; Roller Bearing; Bearing fault; Crest Factor; Variant;Radial Basis Function Network (RBFN); Probabilistic Neural Network (PNN)DOI: 10.3329/jme.v40i2.5353Journal of Mechanical Engineering, Vol. ME 40, No. 2, December 2009 119-130

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

The current state of the legislative implementation of criminal law policy

2020 ◽  
pp. 111-118
Author(s):  
Oleksandr Ostrohliad
Keyword(s):  
Law Enforcement ◽  
Criminal Code ◽  
Practical Significance ◽  
New Approach ◽  
Public Discussion ◽  
Current State ◽  
Criminal Offenses ◽  
Legal Technology ◽  
Logical Semantic

Purpose. The aim of the work is to consider the novelties of the legislative work, which provide for the concept and classification of criminal offenses in accordance with the current edition of the Criminal Code of Ukraine and the draft of the new Code developed by the working group and put up for public discussion. Point out the gaps in the current legislation and the need to revise individual rules of the project in this aspect. The methodology. The methodology includes a comprehensive analysis and generalization of the available scientific and theoretical material and the formulation of appropriate conclusions and recommendations. During the research, the following methods of scientific knowledge were used: terminological, logical-semantic, system-structural, logical-normative, comparative-historical. Results In the course of the study, it was determined that despite the fact that the amendments to the Criminal Code of Ukraine came into force in July of this year, their perfection, in terms of legal technology, raises many objections. On the basis of a comparative study, it was determined that the Draft Criminal Code of Ukraine needs further revision taking into account the opinions of experts in the process of public discussion. Originality. In the course of the study, it was established that the classification of criminal offenses proposed in the new edition of the Criminal Code of Ukraine does not stand up to criticism, since other elements of the classification appear in subsequent articles, which are not covered by the existing one. The draft Code, using a qualitatively new approach to this issue, retains the elements of the previous classification and has no practical significance in law enforcement. Practical significance. The results of the study can be used in law-making activities to improve the norms of the current Criminal Code, to classify criminal offenses, as well as to further improve the draft Criminal Code of Ukraine.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

A New Approach for the Analysis of Mixture Toxicity Data

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2345-2348 ◽  
Author(s):  
C. N. Haas
Keyword(s):  
Quantitative Analysis ◽  
Mixture Toxicity ◽  
New Method ◽  
Metal Exposure ◽  
Positive Interactions ◽  
Toxicity Data ◽  
Data Set ◽  
New Approach ◽  
Negative Interactions

A new method for the quantitative analysis of multiple toxicity data is described and illustrated using a data set on metal exposure to copepods. Positive interactions are observed for Ni-Pb and Pb-Cr, with weak negative interactions observed for Ni-Cr.

scholarly journals BIM for Existing Construction: A Different Logic Scheme and an Alternative Semantic to Enhance the Interoperabilty

2021 ◽  
Vol 11 (4) ◽  
pp. 1855
Author(s):  
Franco Guzzetti ◽  
Karen Lara Ngozi Anyabolu ◽  
Francesca Biolo ◽  
Lara D’Ambrosio
Keyword(s):  
Project Management ◽  
Building Information Modeling ◽  
Information Modeling ◽  
Historical Buildings ◽  
Architectural Heritage ◽  
New Approach ◽  
Building Information ◽  
Heritage Building ◽  
New Construction

In the construction field, the Building Information Modeling (BIM) methodology is becoming increasingly predominant and the standardization of its use is now an essential operation. This method has become widespread in recent years, thanks to the advantages provided in the framework of project management and interoperability. Hoping for its complete dissemination, it is unthinkable to use it only for new construction interventions. Many are experiencing what happens with the so-called Heritage Building Information Modeling (HBIM); that is, how BIM interfaces with Architectural Heritage or simply with historical buildings. This article aims to deal with the principles and working methodologies behind BIM/HBIM and modeling. The aim is to outline the themes on which to base a new approach to the instrument. In this way, it can be adapted to the needs and characteristics of each type of building. Going into the detail of standards, the text also contains a first study regarding the classification of moldable elements. This proposal is based on current regulations and it can provide flexible, expandable, and unambiguous language. Therefore, the content of the article focuses on a revision of the thinking underlying the process, also providing a more practical track on communication and interoperability.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

2021 ◽  
Author(s):  
Xiongzhi Ai ◽  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Yu Fu
Keyword(s):  
Papillary Carcinoma ◽  
Network Model ◽  
Reliability And Validity ◽  
Thyroid Papillary Carcinoma ◽  
Data Set ◽  
Ultrasonic Image ◽  
First Choice ◽  
Thyroid Papillary ◽  
Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

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