Outlier Detection using Granular Box Regression Methods

نویسنده

  • M. Pandiaraj
چکیده

Granular computing (GrC) is an emerging computing paradigm of information processing. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of knowledge from information. Granular computing is more a theoretical perspective, it encourages an approach to data that recognizes and exploits the knowledge present in data at various levels of resolution or scales. Granular computing provides a rich variety of algorithms including methods derived from interval mathematics, fuzzy and rough sets and others. Within this framework granular box regression was proposed recently. The core idea of granular box regression is to determine a fuzzy graph by embedding a given dataset into a predefined number of “boxes”. Granular box regression utilizes intervals a challenge is the detection of outliers. In this paper, we propose borderline method and residual method to detect outliers in granular box regression. We also apply these methods to artificial as well as to real data of motor insurance.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Granular Box Regression Methods for Outlier Detection

Granular computing (GrC) is an emerging computing paradigm of information processing. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of knowledge from information. Granular computing is more a theoretical perspective, it encourages an approach to data that recognizes and exploits the knowledge...

متن کامل

Outlier Detection Based on Granular Computing

As an emerging conceptual and computing paradigm of information processing, granular computing has received much attention recently. Many models and methods of granular computing have been proposed and studied. Among them was the granular computing model using information tables. In this paper, we shall demonstrate the application of this granular computing model for the study of a specific dat...

متن کامل

Selection of Best Outlier Detection Method Using Regression Analysis

Outliers are unusual data values that are inconsistent with most of the records. Such non-representative records can seriously affect the model to be produced, so detecting outlier is a significant job to achieve higher accuracy. Several outlier detection methods are used in literature for real as well as simulated data sets. The aim of this study is to compare the two outlier detection method ...

متن کامل

Outlier Detection in Survival Analysis

Outlier detection is an important task in many data-mining applications. In this paper, we present two parametric outlier detection methods for survival data. Both methods propose to perform outlier detection in a multivariate setting, using the Cox regression as the model and the concordance c-index as a measure of goodness of fit. The first method is a single-step procedure that presents a de...

متن کامل

Applying Artificial Immune System for Outlier Detection: A Comparative Study

Outlier detection is a data mining method for discovering exceptional, abnormal or suspiciously unusual samples in a data set. Outliers typically represent the data rich but information poor dilemma. Data mining methods are applied to solve this problem in broad range of application fields like credit card fraud detection, network intrusion detection, error extraction, clinical disease research...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015