An experimental investigation of the effect of discrete attributes on the precision of classification methods

Entezari‐Maleki, Reza; Iranmanesh, Seyyed Mehdi; Minaei-Bidgoli, Behrouz

doi:10.1109/icict.2009.5267189

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Therefore, it is possible to qualitatively model a series of continuous values if a label is assigned to them. Some studies [4] have shown that executing a prior process to discretize continuous features is more efficient than working directly with the continuous values. The discretization process reduces the computation memory usage and time in the application that develops classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

An adaptive methodology to discretize and select features

Concepción

Abril

Morillo

et al. 2013

AIR

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A lot of significant data describing the behavior or/and actions of systems can be collected in several domains. These data define some aspects, called features, that can be clustered in several classes. A qualitative or quantitative value for each feature is stored from measurements or observations. In this paper, the problem of finding independent features for getting the best accuracy on classification problems is considered. Obtaining these features is the main objective of this work, where an automatic method to select features is proposed. The method extends the functionality of Ameva coefficient to use it in other tasks of machine learning where it has not been defined.

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Section: Introductionmentioning

confidence: 99%

An adaptive methodology to discretize and select features

Concepción

Abril

Morillo

et al. 2013

AIR

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“…GIS software is the core of the system for the implementation of the various operations of the GIS functionality, including data entry, data processing, database management, spatial analysis and graphical users' interface, which can be used as a platform for building geographic information application systems. Their representative products including ArcGIS, MapGIS, SuperGIS, GeoMedia, GeoStar and MapInfo [3][4] etc.…”

Section: Introductionmentioning

confidence: 99%

Architecture Earthquake Disaster Simulation Based on Three-Dimension GIS Algorithm

Pang

Chen

Huang

2013

AMR

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GIS geographic information system algorithm are able to be used in the disaster initialization period, middle and rescue stage for the advanced two-dimensional simulation technology with its geographical model as well as providing us with accurate and quick geographical indicators, with the estimation of building distribution and mountain damaged condition, can bring us extremely high value in use. But the traditional GIS algorithm also has tremendous convenience, namely we can't form accurate analysis of the buildings and streets form three dimensional regions, we cannot tell the difference between topographic change quantities as well, resulting in certain circumstances that the algorithm is no practical. This article puts forward 3d and 2d embedded GIS algorithm according to the satellite remote sensing technology, which can be used to cope with buildings and streets for disaster situation simulation and analysis, through this algorithm we are able to obtain the original data before a disaster, disaster failure scene and the distribution of post-disaster construction. With verification, this algorithm has high practical value and can shorten the fitting time of data.

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“…An experimental investigation of the effect of discrete attributes on the precision of classification methods based on Area Under Curve (AUC) and found with increasing the number of the discrete attributes or with increasing the number of values in discrete attributes, the AUC of logistic regression is increased but linear classifier"s AUC decreases, and AUC of the naïve-Bayes classifier remains constant [6].…”

Section: Literature Surveymentioning

confidence: 99%

Data Classification based on Decision Tree, Rule Generation, Bayes and Statistical Methods: An Empirical Comparison

Saha¹,

Nandi²

2015

IJCA

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In this paper, twenty well known data mining classification methods are applied on ten UCI machine learning medical datasets and the performance of various classification methods are empirically compared while varying the number of categorical and numeric attributes, the types of attributes and the number of instances in datasets. In the performance study, Classification Accuracy (CA), Root Mean Square Error (RMSE) and Area Under Curve (AUC) of Receiver"s Operational Characteristics (ROC) is used as the metric and come up with some findings: (i) performance of classification methods depends upon the type of dataset variables or attributes such as categorical, numeric and both (mixed), (ii) performance of classification methods on categorical attributes is superior than on numeric attributes of a dataset, (iii) classification accuracy, RMSE and AUC of a classification method depends on the number of instances in datasets, (iv) classification performance decreases in case of instances decreases for both categorical as well as numeric datasets, (v) top three classification methods are established after comparing the performance of twenty different classification methods for the categorical, numeric and both (mixed) attribute datasets, (vi) out of these twenty different classification methods Bayes Net, Naïve Bayes, Classification Via Regression, Logistic Regression and Random Forest method performs best on these medical datasets.

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An experimental investigation of the effect of discrete attributes on the precision of classification methods

Cited by 5 publications

References 21 publications

An adaptive methodology to discretize and select features

An adaptive methodology to discretize and select features

Architecture Earthquake Disaster Simulation Based on Three-Dimension GIS Algorithm

Data Classification based on Decision Tree, Rule Generation, Bayes and Statistical Methods: An Empirical Comparison

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