On the optimal number of features in the classification of multivariate Gaussian data

1978 ◽  
Vol 10 (5-6) ◽  
pp. 365-374 ◽  
Author(s):  
A.K. Jain ◽  
W.G. Waller
Keyword(s):  
Optimal Number ◽  
Multivariate Gaussian ◽  
Gaussian Data

scholarly journals A Novel Hybrid Framework for Optimal Feature Selection and Classification of Human Activity Recognition

2018 ◽  
Vol 7 (3.8) ◽  
pp. 63
Author(s):  
Nilam Dhatrak ◽  
Anil Kumar Dudyala
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Optimal Number ◽  
Health Concern ◽  
Hybrid Framework ◽  
Optimal Feature Selection ◽  
Optimal Feature ◽  
Two Hybrid

In today’s world individuals health concern has improved a lot with the help of advancement in the technology. To monitor an age old person or a person with disability, now-a-days modern wearable smartphone devices are available in the market which are equipped with good collection of built in sensors that can be used for Human Activity Recognition (HAR). These type of devices generate lot of data with many number of features. When this data is used for classification, the classifier may be over trained or will definitely give high error rate. Hence, in this paper, we propose a two hybrid frameworks which gives us optimal number of features that can be used with different classifiers to recognize the Human Activity accurately. It is observed from our experiments that SVM was able to classify the HAR accurately.  

Simultaneous Clustering and Classification of Function Recovery Patterns of Ischemic Stroke

2020 ◽  
Vol 10 (6) ◽  
pp. 1401-1407
Author(s):  
Hyungtai Kim ◽  
Minhee Lee ◽  
Min Kyun Sohn ◽  
Jongmin Lee ◽  
Deog Yung Kim ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Classification Performance ◽  
Optimal Number ◽  
Clinical Test ◽  
Data Set ◽  
Number Of Clusters ◽  
Function Recovery ◽  
Clustering And Classification ◽  
Class Labels

This paper shows the simultaneous clustering and classification that is done in order to discover internal grouping on an unlabeled data set. Moreover, it simultaneously classifies the data using clusters discovered as class labels. During the simultaneous clustering and classification, silhouette and F1 scores were calculated for clustering and classification, respectively, according to the number of clusters in order to find an optimal number of clusters that guarantee the desired level of classification performance. In this study, we applied this approach to the data set of Ischemic stroke patients in order to discover function recovery patterns where clear diagnoses do not exist. In addition, we have developed a classifier that predicts the type of function recovery for new patients with early clinical test scores in clinically meaningful levels of accuracy. This classifier can be a helpful tool for clinicians in the rehabilitation field.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

2007 ◽  
Vol 38 (3) ◽  
pp. 303-314 ◽  
Author(s):  
K. Srinivasa Raju ◽  
D. Nagesh Kumar
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Artificial Neural Networks ◽  
Optimal Number ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Homogeneous Groups ◽  
Index Approach ◽  
Artificial Neural

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies–Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

scholarly journals Error rates in spatial classification of Gaussian data with random labeling

2010 ◽  
Vol 51 ◽  
Author(s):  
Lijana Stabingienė ◽  
Kęstutis Dučinskas
Keyword(s):  
Random Field ◽  
Error Rate ◽  
Field Model ◽  
Gaussian Random Field ◽  
Parametric Uncertainty ◽  
Error Rates ◽  
Spatial Classification ◽  
Random Labeling ◽  
Gaussian Data

In spatial classification it is usually assumed that features observations given labels are independently distributed. We have retracted this assumption by proposing stationary Gaussian random field model for features observations. The label are assumed to follow Disrete Random Field (DRF) model. Formula for exact error rate based on Bayes discriminant function (BDF) is derived. In the case of partial parametric uncertainty (mean parameters and variance are unknown), the approximation of the expected error rate associated with plug-in BDF is also derived. The dependence of considered error rates on the values of range and clustering parameters is investigated numerically for training locations being second-order neighbors to location of observation to be classified.

scholarly journals Illiteracy Classification Using K Means-Naïve Bayes Algorithm

2018 ◽  
Vol 2 (3) ◽  
pp. 153 ◽  
Author(s):  
Muhammad Firman Aji Saputra ◽  
Triyanna Widiyaningtyas ◽  
Aji Prasetya Wibawa
Keyword(s):  
Error Rate ◽  
Naive Bayes ◽  
Research Result ◽  
Naïve Bayes ◽  
Optimal Number ◽  
Testing Method ◽  
The World ◽  
Bayes Algorithm ◽  
Number Of Classes

Illiteracy is an inability to recognize characters, both in order to read and write. It is a significant problem for countries all around the world including Indonesia. In Indonesia, illiteracy rate is generally set as an indicator to see whether or not education in Indonesia is successful. If this problem is not going to be overcome, it will affect people’s prosperity. One system that has been used to overcome this problem is prioritizing the treatment from areas with the highest illiteracy rate and followed by areas with lower illiteracy rate. The method is going to be a way easier to be applied if it is supported by classification process. Since the classification process needs a class, and there has not been any fine classification of illiteracy rate, there is needed a clustering process before classification process. This research is aimed to get optimal number of classes through clustering process and know the result of illiteracy classification process. The clustering process is conducted by using k means algorithm, and for the classification process is conducted by using Naïve Bayes algorithm. The testing method used to assess the success of classification process is 10-fold method. Based on the research result, it can be concluded that the optimal illiteracy classes are three classes with the classification accuracy value of 96.4912% and error rate value of 3.5088%. Whereas the classification with two classes get the accuracy value of 93.8596% and error rate value of 6.1404%. And for the classification with five classes get the accuracy value of 90.3509% and error rate value of 9.6491%.

Major aspects of photo tourism in the yamal-nenets autonomous district

10.12737/7471 ◽  
2014 ◽  
Vol 8 (7) ◽  
pp. 0-0
Author(s):  
Мария Киносян ◽  
Mariya Kinosyan ◽  
Екатерина Иошина ◽  
Ekaterina Ioshina ◽  
Алексей Корнеев ◽  
...  
Keyword(s):  
Social Networks ◽  
Tourism Development ◽  
Optimal Number ◽  
Comfort Level ◽  
Mass Tourism ◽  
Autonomous District ◽  
Definition Of ◽  
Mass Form ◽  
Nenets Autonomous

The article describes the main aspects of the development of photo-tourism in the Yamal-Nenets Autonomous District. It is shown that this type of tourism is new, so it does not appear in any of existing regulations. The authors have attempted to give a definition of this type of tourism; show its main consumers. Presented are the factors that affect the emergence and development of photo-tourism. As part of this work the classification of photo tours is suggested, which provides information on the basic criteria required for forming such tours. These criteria include: the age of tourists, comfort level, duration of the tour, the form of the tour, as well as the objects for making photos. The optimal number of participants in the photo tour is determined as 1-3 people for individual tours and 3-8 for group tours). The authors prove the possibility of the development of photo-tourism in the Yamal-Nenets Autonomous District. To do this, an analysis of the main indicators of tourism development in the region is conducted and identified are tourist resources necessary for the development of photo-tourism. The main problems hindering the development of photo-tourism in the Yamal-Nenets Autonomous District, and their solutions are also suggested. In conclusion, it is concluded that the photo-tourism, of course, is not a mass form of tourism, so tourist traffic statistics are not kept, since the number of arrivals with objectives of photo-tourism is insignificant. Currently, however, for most tourists mass tourism programs and direction are becoming unpopular, and in this regard popularization of unusual and unfamiliar forms of recreation, including photo-tourism, is increasing. Thanks to advances in technologies, promotion of social networks and advances in Internet photo art is now considered "fashionable". All this must surely affect the development of photo-tourism, in particular in the Yamal-Nenets Autonomous District.

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