An AI based approach to multiple census data analysis for feature selection

Shanmuganathan, Subana; Li, Yan

doi:10.3233/jifs-169017

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Related to economics and business, there is a discovery of preferences in stock trading [118], focused on this approach of "SOM + decision trees". To conclude this section, we find some similarities in the approach with our research for the selection of properties in the analysis of census data by SOM and decision trees [80,119].…”

Section: Hybrid Model-som and Decision Treesmentioning

confidence: 77%

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

Abarca-Álvarez

Reinoso-Bellido

Campos-Sánchez

2019

IJGI

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Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information.

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Section: Hybrid Model-som and Decision Treesmentioning

confidence: 77%

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

Abarca-Álvarez

Reinoso-Bellido

Campos-Sánchez

2019

IJGI

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“…Con vinculación a la Economía y empresa hallamos un análisis de la cartera de clientes (Yao, Holmbom, Eklund, & Back, 2010), o el descubrimiento de las preferencias en el comercio de acciones (Tsai, Lin, & Wang, 2009), todos ellos enfocados en este planteamiento de SOM + árboles de decisión. Finalmente con cierta similitud en el enfoque con nuestra investigación podemos encontrar la selección de propiedades en el análisis de datos censales mediante SOM y árboles de decisión (Shanmuganathan & Li, 2016).…”

Section: Construcción De Modelos Predictivos Mediante Aprendizaje Supunclassified

Methodology of Decision Support through GIS and Artificial Intelligence: Implementation for Demographic Characterization of Andalusia based on Dwelling

Abarca-Álvarez¹,

Campos-Sánchez²,

Reinoso-Bellido³

2017

est

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Resumenos Sistemas de Información Geográfica (SIG) han sido ampliamente utilizados para el almacenamiento y gestión de la información territorial, mostrándose especialmente útiles para el análisis y para la verificación de hipótesis previamente formuladas y con componentes espaciales relevantes. Existen metodologías heurísticas que en contextos como los actuales, de sobre-abundancia de datos, permiten evidenciar sus coherencias, sin requerir necesariamente hipótesis o formulaciones previas para generar conocimiento. Se propone el uso combinado de (i) técnicas procedentes de la Inteligencia Artificial, como son las Redes Neuronales Artificiales (ANN) del tipo Mapa Auto-organizado (SOM), que han demostrado ser muy eficaces y robustas clasificando y caracterizando perfiles en los datos; integradas con (ii) técnicas de Machine Learning como son los árboles de decisión, singularmente funcionales en la creación de modelos predictivos e interpretables para formular hipótesis explicativas de los perfiles anteriores a partir de otras variables diferenciadas. La investigación plantea combinar SIG, SOM y árboles de decisión para la construcción de modelos explicativos de los perfiles demográficos y sociales de Andalucía, a partir de datos de bajo coste sobre la dimensión residencial. Se verifica la viabilidad de tales modelos predictivos y su alto valor para la comprensión y para la toma de decisiones sobre tales territorios.Palabras clave: árbol de decisión SIG, DSS, mapa auto-organizado. Abstract:Geographic Information Systems (GIS) have been widely used for the storage and management of territorial information, being especially useful for the analysis and verification of previously formulated hypotheses and coexisting with relevant spatial components. There are heuristic methodologies that, in contexts such as the present one, of data over-abundance, allow showing their coherence, not necessarily requiring hypotheses or previous formulations to generate knowledge. The combined use of (i) Artificial Intelligence techniques such as the Artificial Neural Network (ANN), namely the Self-Organized Maps (SOM), is proposed. They are very effective and robust by classifying and characterizing profiles in the data. They interact with (ii) machine learning techniques such as decision trees, which are singularly functional in the creation of predictive and interpretable models, with the intention of formulating explanatory hypotheses of the previous profiles, working with other different variables. The research proposes the combination of GIS, SOM and decision trees for the construction of explanatory models of the demographic and social profiles of Andalusia, based on low cost data on the residential dimension. The feasibility of such predictive models and their great value for understanding and as decision support on such territories are evaluated satisfactorily.

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Risk Profiles of Financial Service Portfolio for Women Segment Using Machine Learning Algorithms

Lozano-Medina

Hervert-Escobar

Hernández-Gress

2020

Lecture Notes in Computer Science

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An AI based approach to multiple census data analysis for feature selection

Cited by 4 publications

References 10 publications

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

Methodology of Decision Support through GIS and Artificial Intelligence: Implementation for Demographic Characterization of Andalusia based on Dwelling

Risk Profiles of Financial Service Portfolio for Women Segment Using Machine Learning Algorithms

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