Evaluation of discernibility matrix based reduct computation techniques

Alikhashashneh, Enas A.; Al-Radaideh, Qasem A.

doi:10.1109/csit.2013.6588762

Cited by 5 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper does not consider attributes/criteria reduction which is an important optimization procedure. In [3], authors discussed and evaluated two Rough set approaches that use the Discernibility Matrix (DM) and some heuristics to compute reducts set; the two algorithms are the Johnson and Object Reduct using Attribute Weighting technique algorithm (ORAW). The ORAW algorithm computes three weighting mechanism (Local Weighting, Global Weighting, and finally Cardinality Value) for each feature that using it further in selecting the most significant features from the actual feature set of a dataset.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Prediction of Suitable Crop Selection for Agriculture Development Using Data Mining Classification Techniques

K¹,

Mahitha²,

N³

et al. 2018

IJET

View full text Add to dashboard Cite

Agriculture is analytically the vast economic sector and is an important aspect in the economic growth of India. It is the only cause of living for about two-thirds of the population in India. It is very essential for the farmers to choose a crop that best suits the land being used for cultivation. The criteria to be considered in order to decide the crops that best suitable for the land are soil, water and season. The best suitable crop for the land can be predicted based on the agriculture data collected from the agriculture experts or from the farmers. Our paper provides a survey of the various classification techniques and classifiers used for the prediction of suitable crop selection for agriculture development. Farmers should get benefited by cultivating the best fitting crops rather than cultivating the unsuitable crops.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A Survey on Prediction of Suitable Crop Selection for Agriculture Development Using Data Mining Classification Techniques

K¹,

Mahitha²,

N³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…They proposed an improved Naïve Bayes classifier technique and explored the use of genetic algorithms (GAs) for selecting a subset of the input features in the classification. [13] described Johnson selection algorithm and the Object Reduct using Feature Weighting technique (ORFW) for reduct computation.…”

Section: Definition 6 Reduct and Corementioning

confidence: 99%

Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

Valarmathi

2016

CIT. J. comput. inf. technol.

View full text Add to dashboard Cite

Precise rainfall forecasting is a common challenge across the globe in meteorological predictions. As rainfall forecasting involves rather complex dynamic parameters, an increasing demand for novel approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST) has attracted a wide variety of scientific applications and is extensively adopted in decision support systems. Although there are several weather prediction techniques in the existing literature, identifying significant input for modelling effective rainfall prediction is not addressed in the present mechanisms. Therefore, this investigation has examined the feasibility of using rough set based feature selection and data mining methods, namely Naïve Bayes (NB), Bayesian Logistic Regression (BLR), Multi-Layer Perceptron (MLP), J48, Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM), to forecast rainfall. Feature selection or reduction process is a process of identifying a significant feature subset, in which the generated subset must characterize the information system as a complete feature set. This paper introduces a novel rough set based Maximum Frequency Weighted (MFW) feature reduction technique for finding an effective feature subset for modelling an efficient rainfall forecast system. The experimental analysis and the results indicate substantial improvements of prediction models when trained using the selected feature subset. CART and J48 classifiers have achieved an improved accuracy of 83.42% and 89.72%, respectively. From the experimental study, relative humidity2 (a4) and solar radiation (a6) have been identified as the effective parameters for modelling rainfall prediction.

show abstract

A Rough Set Based Approach for Web User Profiling

Dhaka

Chakraverty

2018

Data Science and Analytics

View full text Add to dashboard Cite

Evaluation of discernibility matrix based reduct computation techniques

Cited by 5 publications

References 14 publications

A Survey on Prediction of Suitable Crop Selection for Agriculture Development Using Data Mining Classification Techniques

A Survey on Prediction of Suitable Crop Selection for Agriculture Development Using Data Mining Classification Techniques

Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

A Rough Set Based Approach for Web User Profiling

Contact Info

Product

Resources

About