Feature selection based on cluster and variability analyses for ordinal multi-class classification problems

Lin, Hung-Yi

doi:10.1016/j.knosys.2012.07.018

Cited by 22 publications

(3 citation statements)

References 59 publications

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“…The v-SVM model in [38] added a parameter to increase the cardinality of the data as expressed in Eq. (7).…”

Section: Fire Weather Indexmentioning

confidence: 99%

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Model Classification of Fire Weather Index using the SVM-FF Method on Forest Fire in North Sumatra, Indonesia

Manalu,

Sitompul,

Mawengkang

et al. 2023

IJACSA

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As a tropical country, Indonesia is situated in Southeast Asia nation has vast forests. Forest fire occur busy vary due to land conditions and forest conditions in drought season. The indicator used mitigated potential forest fire is to study the indicator behavior of the fire weather index (FWI). The data is gathered from the observation station in north Sumatra province, computation and estimation FWI by Canadian Forest Fire Weather Index based on the data gathered. It is found that there is gathered outlier data. to hope will it, it is necessary to conduct classification and predict this of the dataset by machine learning approach using Support Vector Machine Forest Fire (SVM-FF), which is a further development of the previous models, known as the c-SVM and v-SVM. This method includes a balancing parameter by determining the lower and upper limits of a support vector. Furthermore, it allowed the balancing parameter value to be negative. The results showed that the classification of FWI was at low, medium, high, and extreme levels. The low FWI value has an average of 0.5 which is in the 0 to 1 interval. There was an increase in the model's accuracy and performance from its predecessor, which include the c-SVM and v-SVM with respective values of 0.96 and 0.89. Meanwhile, it was observed that with the SVM-FF model, the accuracy was quite better with a value of 0.99, indicating that it is useful as an alternative to classify and predict forest fires.

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“…The v-SVM model in [38] added a parameter to increase the cardinality of the data as expressed in Eq. (7).…”

Section: Fire Weather Indexmentioning

confidence: 99%

“…A previous research on machine learning that employed a random forest model [5] includes performance measurement of Forest Fire Prediction [6] [7]. Another one was the application of machine learning for classification and prediction using fire weather index data [8].…”

Section: Introductionmentioning

confidence: 99%

Model Classification of Fire Weather Index using the SVM-FF Method on Forest Fire in North Sumatra, Indonesia

Manalu,

Sitompul,

Mawengkang

et al. 2023

IJACSA

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“…Feature selection [17] is an indispensable methodology applied to decrease the dimensionality issue in data mining activity. Developing several data classification models established on the output from feature selection techniques helps to enrich the classification process's predictive performance.…”

Section: Feature Selectionmentioning

confidence: 99%

A Hyper Meta-Heuristic Cascaded Support Vector Machines for Big Data Cyber-Security

Mylavathi*,

Srinivasan

2019

IJRTE

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At an incredible speed, cyber security evolves in the ever-changing setting of attacks. Organisation processing of information inward and outward is huge in quantity and determining a threat amidst of information is challengeable. Late discovery of such instance is standstill challenge of the meticulous process. Thence, detection of intrusion and its prevention are rising challenge in Big data factors. the information inundation generally incorporate the Big data terms to dataset. The majorly focused issues are industrial oriented in big data challenge. Existing systems for big data cyber security problems are based on Online Support Vector Machines (OSVMs) framework. Bi-objective optimisation problem with primary objectives is designed as OSVMs configuration process for improving accuracy and less complexity of model. Here, a bi-objective optimization is implemented based on an Artificial Bee Colony (ABC). However, Online Support Vector Machines (OSVMs) has issue with computational complexity, and prematurity and local optimum is major problems in ABC algorithm. By overcoming this issue, developed research system designs an Ensemble Support Vector Machine (ESVM) framework for big data cyber security. Initially, the feature selection is done by using improved K-means clustering. Based on the selected features the intrusion detection and malware detection are performed using ESVM approach. In this proposed research work, a bi-objective optimization problem is designed as the ESVM configuration process for improving accuracy and less complexity of model and achieve its objectives. Cuckoo Search (CS) optimization algorithm is implemented for the bi-objective optimization. accuracy, precision, recall and f-measure are the parametric meters compared in proposed research attaining higher performance against existing approaches.

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Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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Due to its good versatility and independence, intelligent optimization algorithm has largely shortened the time of decision-making in large-scale optimization problems of manufacture. However, lower searching time often conflicts with the searching accuracy in most cases. To improve the problem solving capability, research in intelligent optimization algorithm based on different domain characteristics never stopped. From the view of manufacturing, this chapter classified and comprehensively analyzed all kinds of manufacturing optimization problems and their general methods, illustrated the application features and challenges of intelligent optimization algorithm in manufacturing, and summarized the development needs and trends of intelligent optimization algorithm in the field of manufacturing system.

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Feature selection based on cluster and variability analyses for ordinal multi-class classification problems

Cited by 22 publications

References 59 publications

Model Classification of Fire Weather Index using the SVM-FF Method on Forest Fire in North Sumatra, Indonesia

Model Classification of Fire Weather Index using the SVM-FF Method on Forest Fire in North Sumatra, Indonesia

A Hyper Meta-Heuristic Cascaded Support Vector Machines for Big Data Cyber-Security

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

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