Statistical classification

The Comparisons of Classification Accuracy of Statistical Software Performance for Default of Credit Card Clients Meixian Wang University of New Hampshire Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2

already been in uses which are based on the pattern recognitions. The objective of this review paper is to summarize and compare some of the well-known methods and application used in pattern recognition system. Keywords- Pattern recognition, classification, clustering, machine learning, error estimation, neural networks. Introduction A pattern is an entity, that could be given a name and pattern recognition is the study of how machines can observe the environment and make sense of it by differentiating

Before proceeding to review a range of predictive analytic algorithms, it is important to know how critical predictive analytics is to the health care industry. The growth rate of US healthcare expenditures, increasing annually by nearly 5% in real terms over the last decade and a major contributor to the high national debt levels projected over the next two decades. McKinsey estimates that Big Data can enable more than $300 billion savings per year in US healthcare, with two-thirds of that through

all-β, α+β, α/β. So far, several algorithms and efforts have been made to deal with this problem. There are two steps involved in predicting protein structural classes. They are, i) Protein feature representation and ii) Design of algorithm for classification. In earlier studies, the protein sequence features can be represented in different ways such as, Functional Domain Composition (Chou And Cai, 2004), Amino Acids

A HYBRID APPROACH TO POWER THEFT DETECTION Abstract:- Now a day’s electricity theft is a major issue face by all electricity companies. Since electricity theft directly affect the profit made by electricity companies, detection and prevention of electricity theft is necessary. In this paper we are proposing a hybrid approach to detect the electricity theft. We will use SVM and ELM for our approach. Introduction:- As we know electricity theft is a major problem for all electricity companies. This

Emergence networks mimics biological nervous system unleash generations of inventions and discoveries in the artificial intelligent field. These networks have been introduced by McCulloch and Pitts and called neural networks. Neural network’s function is based on principle of extracting the uniqueness of patterns through trained machines to understand the extracted knowledge. Indeed, they gain their experiences from collected samples for known classes (patterns). Quick development of neural networks

data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian

task/workflow design and quality assessment. We take a deeper look at crowdsourcing classification tasks, and explore how task and workflow design can impact the answer quality. Our research is intended to use large knowledge base and citizen science projects as examples and investigate the workflow design considerations and its impact on worker performance as well as overall quality outcome based on statistical, probabilistic, or machine learning models for quality answer prediction, such that optimal

COMPARISON OF CLASSIFIERS CLASSIFIER CATEGORY DESCRIPTION REFERENCE Naive Bayes Probability based classifier This classifier is derived from Naïve Bayes conditional probability. This is suitable for datasets having less number of attributes. [5] Bayesian Net Probability based classifier Network of nodes based on Naïve Bayes classifier is termed as Bayesian Net. This can be applied to larger datasets as compared to Naïve Bayes. [9] Decision Tree (J48) Tree based approach It is enhanced version of

Many researchers have proposed various methodologies for finding best solution. J. Ross Quinlan. In machine learning community, the decision tree algorithms, Quinlan’s ID3 and its successor C4.5: Programs for machine learning are probably the most popular. The various issues related to decision tree are discussed from the initial state of building a tree to methods of pruning, converting trees into rules and handling other problems such as missing attribute values. Apart from that