Corpus ID: 40409471

An Exploration of Classification prediction techniques in data mining: the insurance domain

  title={An Exploration of Classification prediction techniques in data mining: the insurance domain},
  author={Samuel O. Danso},
Towards Prediction of Students’ Academic Performance in Secondary School Using Decision Trees
Prediction of students’ academic performance with high accuracy is useful in many contexts. Institutions would like to know which students are likely to have low academic achievements or needExpand
A Comparative Study in Data Mining: Clustering and Classification Capabilities
A detailed study on the data mining field takes place, followed by a comparative study between clustering and classification techniques, resulting that the integration of clusteringand classification techniques can provide more accurate results than a simple classification technique that classifies datasets with priorly known attributes and classes. Expand
Classifying sentiments of twitter data using Bay’s neural network
A supervised hybrid classification technique is developed using the Bay’s classifier and the Back propagation neural network to find the emotional components of the micro-blog text according to the text sentiments and emotional features. Expand
Financial Trends prediction using the Back Propagation Neural Network and YQL
In this world a amount of things and procedures are impulsive, among the stock market value is an entity which is impulsive. The stock market and their prices are haphazard in impulsive manner thatExpand
A Hybrid Data Model for Prediction of Disaster using Data Mining Approaches
A new data model is proposed which collect the real world knowledge from the web data sources and use with the data mining algorithms for predicting the unpredictable natural disasters and the results show the presentation of the futuremethod is accurate and efficient for prediction with any kind of other data also. Expand
Financial Trends prediction using the Back Propagation Neural Network and YQL
The proposed system is an enhanced version of the traditionally available back propagation neural network which is compared with the traditional BPN model and the performance is found improved and adoptable. Expand
A comparative study of decision Tree and Naïve Bayesian Classifiers on Verbal Autopsy Datasets
Using nanofiltration membranes for recovery of phosphorous with a second type of technology for the recovery of nitrogen is suggest to be a viable process. Expand
A graph theoretic approach to control the traffic congestion on road network
 A graph theoretic approach to control the traffic congestion on road network .................... 1  Android mobile driving assistant for highway driversExpand
Final Grade Prediction of Secondary School Student using Decision Tree
J48 decision tree algorithm is applied on student previous result data to build a model in the form of decision tree which can predict the student final grade and will enable them to take preventive measure. Expand
Improving Cyber Situational Awareness Through Data Mining and Predictive Analytic Techniques
This paper aims to investigate and review current state of CSA improvement through data mining techniques and predictive analytic and offers possible methodology based on datamining techniques which can be used by cyber firms in order to secure themselves against future cyber threats. Expand


The goal of this tutorial is to provide an introduction to data mining techniques appropriate for mining massive datasets using techniques from scalable and high performance computing. Expand
An Introduction to Variable and Feature Selection
The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods. Expand
A Comparative Study on Feature Selection
  • 2002
Pattern Classification
Classification • Supervised – parallelpiped – minimum distance – maximum likelihood (Bayes Rule) > non-parametric > parametric – support vector machines – neural networks – context classification •Expand
Ensemble Methods in Machine Learning
Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly. Expand
Task and Results: Predicting and Explaining Caravan Policy Ownership
  • CoIL Challenge
  • 2000
Pattern Recognition
An empirical comparison of supervised learning algorithms
A large-scale empirical comparison between ten supervised learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps is presented. Expand
Editorial: Special Issues on learning from Imbalance data
  • set. ACM SIGKDD Explorations
  • 2006
Attribute selection methods comparison for classification of diffuse large B-cell lymphoma
  • J. C. Nievola, H. B. Borges
  • Computer Science, Biology
  • Fourth International Conference on Machine Learning and Applications (ICMLA'05)
  • 2005
The results show that most of the combinations from search algorithms and evaluation algorithms within the attribute selection algorithm work well, reducing the number of attributes and leading to improved classification rates. Expand