scholarly journals Combining Akaike’s Information Criterion (AIC) and the Golden-Section Search Technique to find Optimal Numbers of K-Nearest Neighbors

2010 ◽  
Vol 2 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Asha Gowda Karegowda ◽  
M.A. Jayaram ◽  
A.S. Manjunath
Keyword(s):  
Golden Section ◽  
Information Criterion ◽  
Nearest Neighbors ◽  
Search Technique ◽  
Akaike's Information Criterion ◽  
K Nearest Neighbors ◽  
Akaike’S Information Criterion ◽  
Golden Section Search

Cluster Analysis of Antigenic Profiles of Tumors: Selection of Number of Clusters Using Akaike’s Information Criterion

1990 ◽  
Vol 29 (03) ◽  
pp. 200-204 ◽  
Author(s):  
J. A. Koziol
Keyword(s):  
Cluster Analysis ◽  
Basic Problem ◽  
Information Criterion ◽  
Akaike's Information Criterion ◽  
Cell Surface Antigens ◽  
Number Of Clusters ◽  
Akaike’S Information Criterion ◽  
Multinomial Data ◽  
Tumor Types ◽  
Selection Of

AbstractA basic problem of cluster analysis is the determination or selection of the number of clusters evinced in any set of data. We address this issue with multinomial data using Akaike’s information criterion and demonstrate its utility in identifying an appropriate number of clusters of tumor types with similar profiles of cell surface antigens.

Quantification of thermal energy generation in annular hyperbolic porous-finned heat sinks using inverse optimization

2021 ◽  
pp. 095440892110243
Author(s):  
Sagnik Pal ◽  
Ranjan Das
Keyword(s):  
Heat Transfer ◽  
Surface Temperature ◽  
Heat Generation ◽  
Thermal Energy ◽  
Inverse Method ◽  
Golden Section ◽  
Energy Generation ◽  
Heat Sinks ◽  
Search Technique ◽  
Golden Section Search

The present paper introduces an accurate numerical procedure to assess the internal thermal energy generation in an annular porous-finned heat sink from the sole assessment of surface temperature profile using the golden section search technique. All possible heat transfer modes and temperature dependence of all thermal parameters are accounted for in the present nonlinear model. At first, the direct problem is numerically solved using the Runge–Kutta method, whereas for predicting the prevailing heat generation within a given generalized fin domain an inverse method is used with the aid of the golden section search technique. After simplifications, the proposed scheme is credibly verified with other methodologies reported in the existing literature. Numerical predictions are performed under different levels of Gaussian noise from which accurate reconstructions are observed for measurement error up to 20%. The sensitivity study deciphers that the surface temperature field in itself is a strong function of the surface porosity, and the same is controlled through a joint trade-off among heat generation and other thermo-geometrical parameters. The present results acquired from the golden section search technique-assisted inverse method are proposed to be suitable for designing effective and robust porous fin heat sinks in order to deliver safe and enhanced heat transfer along with significant weight reduction with respect to the conventionally used systems. The present inverse estimation technique is proposed to be robust as it can be easily tailored to analyse all possible geometries manufactured from any material in a more accurate manner by taking into account all feasible heat transfer modes.

A Comparison of Segment Retention Criteria for Finite Mixture Logit Models

2003 ◽  
Vol 40 (2) ◽  
pp. 235-243 ◽  
Author(s):  
Rick L. Andrews ◽  
Imran S. Currim
Keyword(s):  
Information Criterion ◽  
Finite Mixture ◽  
Choice Data ◽  
Akaike's Information Criterion ◽  
Experimental Conditions ◽  
Widespread Application ◽  
Akaike’S Information Criterion ◽  
True Number ◽  
Penalty Factor ◽  
Number Of Segments

Despite the widespread application of finite mixture models in marketing research, the decision of how many segments to retain in the models is an important unresolved issue. Almost all applications of the models in marketing rely on segment retention criteria such as Akaike's information criterion, Bayesian information criterion, consistent Akaike's information criterion, and information complexity to determine the number of latent segments to retain. Because these applications employ real-world data in which the true number of segments is unknown, it is not clear whether these criteria are effective. Retaining the true number of segments is crucial because many product design and marketing decisions depend on it. The purpose of this extensive simulation study is to determine how well commonly used segment retention criteria perform in the context of simulated multinomial choice data, as obtained from supermarket scanner panels, in which the true number of segments is known. The authors find that an Akaike's information criterion with a penalty factor of three rather than the traditional value of two has the highest segment retention success rate across nearly all experimental conditions. Currently, this criterion is rarely, if ever, applied in the marketing literature. Experimental factors of particular interest in marketing contexts, such as the number of choices per household, the number of choice alternatives, the error variance of the choices, and the minimum segment size, have not been considered in the statistics literature. The authors show that they, among other factors, affect the performance of segment retention criteria.

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