scholarly journals Cheeger Cut Model for the Balanced Data Classification Problem

2013 ◽  
Author(s):  
Yan-zhou Zhang ◽  
Yan Jiang ◽  
Zhi-Feng Pang
Keyword(s):  
Data Classification ◽  
Classification Problem ◽  
Cheeger Cut

Cheeger Cut Model for the Balanced Data Classification Problem

2013 ◽  
Vol 765-767 ◽  
pp. 730-734
Author(s):  
Yan Zhou Zhang ◽  
Yan Jiang ◽  
Zhi Feng Pang
Keyword(s):  
Numerical Method ◽  
Data Classification ◽  
Classification Problem ◽  
Numerical Examples ◽  
Scaling Parameter ◽  
Self Tuning ◽  
Cheeger Cut ◽  
Tuning Strategy

In this paper we propose a numerical method based on the splitting strategy to solve the Cheeger cut model. In order to improve the classification results, we propose a new self-tuning strategy to choose a robust scaling parameter. Some numerical examples are arranged to illustrate the efficiency of our proposed method.

scholarly journals Deep Learning for Hyperspectral Data Classification through Exponential Momentum Deep Convolution Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qi Yue ◽  
Caiwen Ma
Keyword(s):  
Data Classification ◽  
Classification Problem ◽  
Hyperspectral Data ◽  
Support Vector ◽  
New Classification ◽  
Spatial Features ◽  
Classification Framework ◽  
Deep Convolution Neural Network ◽  
Quantitative Validation ◽  
High Level

Classification is a hot topic in hyperspectral remote sensing community. In the last decades, numerous efforts have been concentrated on the classification problem. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on complex handcrafted features. However, it is rarely known which features are important for the problem. In this paper, a new classification skeleton based on deep machine learning is proposed for hyperspectral data. The proposed classification framework, which is composed of exponential momentum deep convolution neural network and support vector machine (SVM), can hierarchically construct high-level spectral-spatial features in an automated way. Experimental results and quantitative validation on widely used datasets showcase the potential of the developed approach for accurate hyperspectral data classification.

scholarly journals Social Spider Optimization with Tumbling Effect Based Data Classification Model for Stock Price Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 568-578
Keyword(s):  
Stock Price ◽  
Data Classification ◽  
Classification Problem ◽  
Hard Problem ◽  
Np Hard ◽  
Social Spider ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Social Spider Optimization ◽  
Np Hard Problem

In last decade, data classification become more famous which aims to classify the data to a fixed number of classes. The data classification problem is treated as an NP hard problem and different optimization models are presented to resolve it. This paper introduces a social spider optimization (SSO) algorithm with tumbling effect called SSO-T algorithm to solve the data classification problem. First, the SSO algorithm is derived to solve the classification process which is considered as a NP hard problem. Next, to further enhance the exploration process of SSO algorithm, it is modified by the inclusion of tumbling effect, called SSO-T algorithm. For validating the results of the SSO and SSO-T algorithm, a real time problem of stock price prediction (SSP) is employed. For experimentation, the results are validated by testing the SSO and SSO-T algorithms against four datasets such as Dow Jones Index (DJI) dataset, three own datasets gathered from Yahoo finance on the basis of daily, weekly and yearly. The empirical result states that the proposed algorithms perform well and it is noted that the classification performance of the SSO algorithm is increased by the inclusion of tumbling effect.

scholarly journals Social Spider Optimization Algorithm for Effective Data Classification: An Application of Stock Price Prediction

2019 ◽  
Vol 8 (3) ◽  
pp. 1770-1780
Keyword(s):  
Stock Price ◽  
Optimization Problems ◽  
Data Classification ◽  
Classification Problem ◽  
Social Spider ◽  
Jones Index ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Social Spider Optimization ◽  
Time Problem

Presently, data classification has become a hot research topic, intends to categorize the data into a predefined number of classes. The data classification problem has been considered as a NP hard problem and various optimization algorithms are introduced to solve it. Social spider optimization (SSO) algorithm is originally developed to resolve continuous and optimization problems. In line with, it has been altered to manage different kinds of optimization process and also to be employed for data investigation. And, some other studies are also investigated the use of SSO algorithm in different domains. In this paper, we introduce new SSO algorithm particularly applicable for data classification process. In order to validate the performance of the SSO algorithm, a real time problem of stock price prediction (SPP) is employed. For experimentation, the results are validated by testing the SSO algorithm against four datasets such as Dow Jones Index (DJI) dataset, three own datasets gathered from Yahoo finance on the basis of daily, weekly and yearly. The empirical result states that the proposed algorithms perform well and it is noted that the classification performance of the SSO algorithm better than the compared methods.

Data Classification

2017 ◽  
pp. 34-51 ◽  
Author(s):  
A. Sheik Abdullah ◽  
R. Suganya ◽  
S. Selvakumar ◽  
S. Rajaram
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Classification ◽  
Large Data ◽  
Classification Problem ◽  
Data Sets ◽  
Credit Risk Analysis ◽  
Predicted Values ◽  
Class Labels ◽  
The One

Classification is considered to be the one of the data analysis technique which can be used over many applications. Classification model predicts categorical continuous class labels. Clustering mainly deals with grouping of variables based upon similar characteristics. Classification models are experienced by comparing the predicted values to that of the known target values in a set of test data. Data classification has many applications in business modeling, marketing analysis, credit risk analysis; biomedical engineering and drug retort modeling. The extension of data analysis and classification makes the insight into big data with an exploration to processing and managing large data sets. This chapter deals with various techniques, methodologies that correspond to the classification problem in data analysis process and its methodological impacts to big data.

Sign in / Sign up

Export Citation Format

Share Document