Self-organizing maps could improve the classification of Spanish mutual funds

Moreno, David; Marco, Paulina; Olmeda, Ignacio

doi:10.1016/j.ejor.2004.12.018

Cited by 63 publications

(24 citation statements)

References 17 publications

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“…Examples include classifying cognitive performance in schizophrenic patients and healthy individuals (Silver and Shmoish, 2008), mutual funds classification (Moreno et al, 2006), speech quality assessment (Mahdi, 2006), vehicle routing (Ghaziri and Osman, 2006), network intrusion detection (Zhong et al, 2007), anomalous behavior in communication networks (Frota et al, 2007), compounds pattern recognition (Yan, 2006), market segmentation (Kuo et al, 2002) and classifying magnetic resonance brain images (Chaplot et al, 2006). There are many software packages available for analyzing SOM models.…”

Section: Self-organizing Mapsmentioning

confidence: 99%

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Psychographic clustering of blood donors in Egypt using Kohonen's self‐organizing maps

Mostafa

2009

J of Philanthropy and Mktg

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Blood donation has historically been marketed as one of the purest examples of altruistic or pro-social behavior. The recruitment of blood donors is, however, a challenge as transfusion centers worldwide strive to attract and retain blood donors. This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on blood donation in Egypt. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and nonlinear patterns. The results show that major variables affecting blood donation are related to altruistic values, perceived risks of blood donation, blood donation knowledge, attitudes toward blood donation, and intention to donate blood. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.

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Section: Self-organizing Mapsmentioning

confidence: 99%

“…Output neurons will self-organize to an ordered map and neurons with similar weights are placed together. They are connected to adjacent neurons by a neighbourhood relation, dictating the topology of the map (Moreno et al, 2006). The number of neurons can vary from a few dozen to several thousand.…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Psychographic clustering of blood donors in Egypt using Kohonen's self‐organizing maps

Mostafa

2009

J of Philanthropy and Mktg

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“…Output neurons will self-organize to an ordered map and neurons with similar weights are placed together. They are connected to adjacent neurons by a neighborhood relation, dictating the topology of the map [85]. The number of neurons can vary from a few dozen to several thousand.…”

Section: Self-organizing Mapsmentioning

confidence: 99%

“…Due to the unsupervised character of their learning algorithm and the excellent visualization ability, SOMs have been recently used in myriad classification and clustering tasks. Examples include classifying cognitive performance in schizophrenic patients and healthy individuals [100], mutual funds classification [85], speechquality assessment [76], vehicle routing [45], network intrusion detection [122], anomalous behavior in communication networks [44], compounds pattern recognition [118], mature market segmentation (Bigne et al [16], and classifying magnetic resonance brain images [24].…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Mostafa

Toksarı

2011

Neural Comput & Applic

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First performed in 1954, organ transplantation is a universally practiced clinical procedure. This study uses ant colony optimization (ACO), radial basis function neural network (RBFNN), Kohonen's self-organizing maps (SOM), and support vector machines (SVMs) to examine the effect of various cognitive, psychographic, and attitudinal factors on organ donation. ACO, RBFNN, SOM, and SVMs are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are altruistic values, perceived risks/benefits, knowledge, attitudes toward organ donation, and intention to donate organs. The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering complex patterns in the dataset and also shows the classification and clustering abilities of machine-learning systems.

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“…They are connected to adjacent neurons by a neighbourhood relation, dictating the topology of the map (Moreno et al, 2006). The concept of the learning algorithm for SOM is unsupervised and competitive.…”

Section: Self Organizing Mapmentioning

confidence: 99%

A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

Ismail

Shabri

Samsudin³

2012

Hydrol. Earth Syst. Sci.

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Abstract. Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM model for river flow forecasting. The hybrid model uses the SOM algorithm to cluster the entire dataset into several disjointed clusters, where the monthly river flows data with similar input pattern are grouped together from a high dimensional input space onto a low dimensional output layer. By doing this, the data with similar input patterns will be mapped to neighbouring neurons in the SOM's output layer. After the dataset has been decomposed into several disjointed clusters, an individual LSSVM is applied to forecast the river flow. The feasibility of this proposed model is evaluated with respect to the actual river flow data from the Bernam River located in Selangor, Malaysia. The performance of the SOM-LSSVM was compared with other single models such as ARIMA, ANN and LSSVM. The performance of these models was then evaluated using various performance indicators. The experimental results show that the SOM-LSSVM model outperforms the other models and performs better than ANN, LSSVM as well as ARIMA for river flow forecasting. It also indicates that the proposed model can forecast more precisely, and provides a promising alternative technique for river flow forecasting.

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Self-organizing maps could improve the classification of Spanish mutual funds

Cited by 63 publications

References 17 publications

Psychographic clustering of blood donors in Egypt using Kohonen's self‐organizing maps

Psychographic clustering of blood donors in Egypt using Kohonen's self‐organizing maps

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

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