Fast generic selection of features for neural network classifiers

Brill, Frank; Brown, Donald E.; Martin, Worthy N.

doi:10.1109/72.125874

Cited by 193 publications

(95 citation statements)

References 6 publications

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“…Reference [13] used Genetic Algorithm for feature selection in the context of a neural network classifier. GA was configured to use an approximate evaluation in order to reduce significantly the computation required.…”

Section: Related Literaturementioning

confidence: 99%

Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining

Cruz¹,

Gerardo²,

Tanguilig³

2014

IJMO

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Abstract-Extraction of knowledge in agricultural data is a challenging task, from discovering patterns and relationships and interpretation. In order to obtain potentially interesting patterns and relationships from this data, it is therefore essential that a methodology be developed and take advantage of the sets of existing methods and tools available for data mining and knowledge discovery in databases. Data mining is relatively a new approach in the field of agriculture. Accurate information in characterizing crops depends on climatic, geographical, biological and other factors. These are very important inputs to generate characterization and prediction models in data mining. In this study, an efficient data mining methodology based on PCA-GA is explored, presented and implemented to characterize agricultural crops. The method draws improvements to classification problems by using Principal Components Analysis (PCA) as a pre processing method and a modified Genetic Algorithm (GA) as the function optimizer. The fitness function in GA is modified accordingly using efficient distance measures. The approach is to asses, the PCA-GA hybrid data mining method, using various agricultural field data sets, generate data mining classification models and establish meaningful relationships. The experimental results show improved classification rates and generated characterization models for agricultural crops. The domain model outcome may have benefits, to agricultural researchers and farmers. These generated classification models can also be utilized and readily incorporated into a decision support system.

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Section: Related Literaturementioning

confidence: 99%

Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining

Cruz¹,

Gerardo²,

Tanguilig³

2014

IJMO

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show abstract

“…Reference [8] used Genetic Algorithm for feature selection in the context of a neural network classifier. GA was configured to use an approximate evaluation in order to reduce significantly the computation required.…”

Section: Related Literaturementioning

confidence: 99%

An Improved Data Mining Mechanism Based on PCA-GA for Agricultural Crops Characterization

Cruz¹,

Gerardo²,

Tanguilig³

2014

IJCCE

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Abstract-In this study, a data mining method based on PCA-GA is presented to characterize agricultural crops. Specifically it draws improvements to classification problems by using Principal Components Analysis (PCA) as a preprocessing method and a modified Genetic Algorithm (GA) as the function optimizer. The GA performs the optimization process, selecting the most suited set of features that determines the class of a crop it belongs to. The fitness function in GA is studied and modified accordingly using efficient distance measures. The soybean dataset is used in the experiment and results are compared with several classifiers. The experimental results show improved classification rates. This lessens the time consumed of agricultural researchers in characterizing agricultural crops.

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“…Brill et al [17] used sampling to speed up feature selection. Each individual was evaluated using its nearest neighbor error, an operation which is of O(N 2 M) time complexity, for N instances and M features.…”

Section: Sampling and Data Partitioningmentioning

confidence: 99%

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

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The overwhelming amount of data that are now available in any field of research poses new problems for data mining and knowledge discovery methods. Due to this huge amount of data, most of the current data mining algorithms are inapplicable to many real-world problems. Data mining algorithms become ineffective when the problem size becomes very large. In many cases, the demands of the algorithm in terms of the running time are very large, and mining methods cannot be applied when the problem grows. This aspect is closely related to the time complexity of the method. A second problem is linked with performance; although the method might be applicable, the size of the search space prevents an efficient execution, and the resulting solutions are unsatisfactory. Two approaches have been used to deal with this problem: scaling up data mining algorithms and data reduction. However, because data reduction is a data mining task itself, this technique also suffers from scalability problems. Thus, for many problems, especially when dealing with very large datasets, the only way to deal with the aforementioned problems is to scale up the data mining algorithm. Many efforts have been made to obtain methods that can be used to scale up existing data mining algorithms. In this paper, we review the methods that have been used to address the problem of scalability. We focus on general ideas, rather than specific implementations, that can be used to provide a general view of the current approaches for scaling up data mining methods. A taxonomy of the algorithms is proposed, and many examples of different tasks are presented. Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks.

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Fast generic selection of features for neural network classifiers

Cited by 193 publications

References 6 publications

Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining

Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining

An Improved Data Mining Mechanism Based on PCA-GA for Agricultural Crops Characterization

Scaling up data mining algorithms: review and taxonomy

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