Knowledge Rule Discovery Based on Training Data of Rowing

2011 ◽  
Author(s):  
Li Guangjun ◽  
Pei Kejun
Keyword(s):  
Training Data ◽  
Rule Discovery ◽  
Knowledge Rule

A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets

2011 ◽  
Vol 2 (1) ◽  
pp. 49-58
Author(s):  
Periasamy Vivekanandan ◽  
Raju Nedunchezhian
Keyword(s):  
Genetic Algorithm ◽  
Training Data ◽  
Classification Rule ◽  
Rule Discovery ◽  
Classification Rules ◽  
Search Technique ◽  
Natural Evolution ◽  
Ensemble Of Classifiers ◽  
Data Set ◽  
Mining Community

Genetic algorithm is a search technique purely based on natural evolution process. It is widely used by the data mining community for classification rule discovery in complex domains. During the learning process it makes several passes over the data set for determining the accuracy of the potential rules. Due to this characteristic it becomes an extremely I/O intensive slow process. It is particularly difficult to apply GA when the training data set becomes too large and not fully available. An incremental Genetic algorithm based on boosting phenomenon is proposed in this paper which constructs a weak ensemble of classifiers in a fast incremental manner and thus tries to reduce the learning cost considerably.

scholarly journals An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shigeaki Sakurai ◽  
Kyoko Makino ◽  
Shigeru Matsumoto
Keyword(s):  
Stock Price ◽  
Numerical Experiments ◽  
Detection Performance ◽  
Activation Method ◽  
Training Data ◽  
Sequential Data ◽  
Rule Discovery ◽  
Near Future ◽  
News Headlines ◽  
Numerical Change

This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments.

MR-AntMiner: A Novel MapReduce Classification Rule Discovery with Ant Colony Intelligence

2019 ◽  
Vol 23 (5) ◽  
pp. 928-938
Author(s):  
Yun Kong ◽  
Junsan Zhao ◽  
Na Dong ◽  
Yilin Lin ◽  
Lei Yuan ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Programming Model ◽  
Learning Algorithm ◽  
Experimental Studies ◽  
Ant Colony ◽  
Training Data ◽  
Classification Rule ◽  
Communication Model ◽  
Rule Discovery ◽  
Classification Problems

Ant colony optimization (ACO) algorithms have been successfully applied to data classification problems that aim at discovering a list of classification rules. However, on the one hand, the ACO algorithm has defects including long search times and convergence issues with non-optimal solutions. On the other hand, given bottlenecks such as memory restrictions, time complexity, or data complexity, it is too hard to solve a problem when its scale becomes too large. One solution for this issue is to design a highly parallelized learning algorithm. The MapReduce programming model has quickly emerged as the most common model for executing simple algorithmic tasks over huge volumes of data, since it is simple, highly abstract, and efficient. Therefore, MapReduce-based ACO has been researched extensively. However, due to its unidirectional communication model and the inherent lack of support for iterative execution, ACO algorithms cannot easily be implemented on MapReduce. In this paper, a novel classification rule discovery algorithm is proposed, namely MR-AntMiner, which can capitalize on the benefits of the MapReduce model. In order to construct quality rules with fewer iterations as well as less communication between different nodes to share the parameters used by each ant, our algorithm splits the training data into some subsets that are randomly mapped to different mappers; then the traditional ACO algorithm is run on each mapper to gain the local best rule set, and the global best rule list is produced in the reducer phase according to a voting mechanism. The performance of our algorithm was studied experimentally on 14 publicly available data sets and further compared to several state-of-the-art classification approaches in terms of accuracy. The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets. Furthermore, experimental studies show the feasibility and the good performance of the proposed parallelized MR-AntMiner algorithm.

A Note on Document Classification with Small Training Data

2011 ◽  
Vol 131 (8) ◽  
pp. 1459-1466
Author(s):  
Yasunari Maeda ◽  
Hideki Yoshida ◽  
Masakiyo Suzuki ◽  
Toshiyasu Matsushima
Keyword(s):  
Document Classification ◽  
Training Data

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Cardiology Fellowship Education in the Era of High-density Training, Data Tracking, and Quality Measures

2011 ◽  
Vol 9 (2) ◽  
pp. 99
Author(s):  
Alex J Auseon ◽  
Albert J Kolibash ◽  
◽  
Keyword(s):  
Ohio State University ◽  
Training Data ◽  
State University ◽  
Training Requirements ◽  
Healthcare Environment ◽  
Medical Centers ◽  
Work Hour ◽  
The Ohio State University ◽  
Data Tracking

Background:Educating trainees during cardiology fellowship is a process in constant evolution, with program directors regularly adapting to increasing demands and regulations as they strive to prepare graduates for practice in today’s healthcare environment.Methods and Results:In a 10-year follow-up to a previous manuscript regarding fellowship education, we reviewed the literature regarding the most topical issues facing training programs in 2010, describing our approach at The Ohio State University.Conclusion:In the midst of challenges posed by the increasing complexity of training requirements and documentation, work hour restrictions, and the new definitions of quality and safety, we propose methods of curricula revision and collaboration that may serve as an example to other medical centers.

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

Semi-supervised Multi-task Network For Image Aesthetic Assessment

2020 ◽  
Vol 2020 (8) ◽  
pp. 188-1-188-7
Author(s):  
Xiaoyu Xiang ◽  
Yang Cheng ◽  
Jianhang Chen ◽  
Qian Lin ◽  
Jan Allebach
Keyword(s):  
Neural Network ◽  
Semantic Content ◽  
Training Data ◽  
Score Distribution ◽  
Generalization Ability ◽  
Subjective Preference ◽  
Ranking Tasks

Image aesthetic assessment has always been regarded as a challenging task because of the variability of subjective preference. Besides, the assessment of a photo is also related to its style, semantic content, etc. Conventionally, the estimations of aesthetic score and style for an image are treated as separate problems. In this paper, we explore the inter-relatedness between the aesthetics and image style, and design a neural network that can jointly categorize image by styles and give an aesthetic score distribution. To this end, we propose a multi-task network (MTNet) with an aesthetic column serving as a score predictor and a style column serving as a style classifier. The angular-softmax loss is applied in training primary style classifiers to maximize the margin among classes in single-label training data; the semi-supervised method is applied to improve the network’s generalization ability iteratively. We combine the regression loss and classification loss in training aesthetic score. Experiments on the AVA dataset show the superiority of our network in both image attributes classification and aesthetic ranking tasks.

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