An Improved Feature Selection and Classification using Decision Tree for Crop Datasets

Chouhan, Surabhi; Singh, Divakar; Singh, Anju

doi:10.5120/ijca2016909966

Cited by 9 publications

(8 citation statements)

References 7 publications

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“…The experimental results show that the proposed technique offers higher accuracy than other methods. Surabhi Chouhan et al [22], proposed a hybrid combination method of applying the Particle Swarm Optimization -Support Vector Machine (PSO-SVM) to select features from a dataset. Assorted benchmark datasets were tested with this technique.…”

Section: Literature Survey and Justification For The Proposed Workmentioning

confidence: 99%

Crop prediction based on soil and environmental characteristics using feature selection techniques

Suruliandi

Mariammal

Raja

2021

Mathematical and Computer Modelling of Dynamical Systems

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Earlier, crop cultivation was undertaken on the basis of farmers' hands-on expertise. However, climate change has begun to affect crop yields badly. Consequently, farmers are unable to choose the right crop/s based on soil and environmental factors, and the process of manually predicting the choice of the right crop/s of land has, more often than not, resulted in failure. Accurate crop prediction results in increased crop production. This is where machine learning playing a crucial role in the area of crop prediction. Crop prediction depends on the soil, geographic and climatic attributes. Selecting appropriate attributes for the right crop/s is an intrinsic part of the prediction undertaken by feature selection techniques. In this work, a comparative study of various wrapper feature selection methods are carried out for crop prediction using classification techniques that suggest the suitable crop/s for land. The experimental results show the Recursive Feature Elimination technique with the Adaptive Bagging classifier outperforms the others.

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Section: Literature Survey and Justification For The Proposed Workmentioning

confidence: 99%

Crop prediction based on soil and environmental characteristics using feature selection techniques

Suruliandi

Mariammal

Raja

2021

Mathematical and Computer Modelling of Dynamical Systems

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“…Mushroom and Soyabean benchmark datasets are considered. The outcome reveals that the proposed hybrid methodology performs better than other presented methodologies [10].…”

Section: Related Workmentioning

confidence: 90%

“…The accuracy calculation is a very essential component for any classification algorithm. It will testify the past active classification algorithms as "Right" or else "Wrong" [30].…”

Section: Performance Measuresmentioning

confidence: 99%

Feature Selection Method based on Fisher’s Exact Test for Agricultural Data

2019

IJRTE

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This paper is aimed to analyze the feature selection process based on different statistical methods viz., Correlation, Gain Ratio, Information gain, OneR, Chi-square MapReduce model, Fisher’s exact test for agricultural data. During the recent past, Fishers exact test was commonly used for feature selection process. However, it supports only for small data set. To handle large data set, the Chi square, one of the most popular statistical methods is used. But, it also finds irrelevant data and thus resultant accuracy is not as expected. As a novelty, Fisher’s exact test is combined with Map Reduce model to handle large data set. In addition, the simulation outcome proves that proposed fisher’s exact test finds the significant attributes with more accurate and reduced time complexity when compared to other existing methods.

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“…Using group structure technique they performed the filtering. This improved the accuracy and achieved relatively better classification performance [6].…”

Section: Related Workmentioning

confidence: 93%

Chi-Square MapReduce Model for Agricultural Data

Rȧjeswari

Suthendran

2018

JCSM

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Nowadays, agriculture plays a very significant role in economic growth. Decision making, crop selection and crop yield are the important issues in agriculture productions. Agricultural automation has lead to an incredible growth of software and applications to access the information. Agriculture database contains the farmer's details, land details, soil nutrient details, water levels details and etc. When the data set contains irrelevant, redundant and noisy data then it degrades the performance of the classifier model. The feature selection algorithm is used to improve the performance by selecting the relevant attributes and removing the irrelevant attributes from the database. In this paper, a novel idea is proposed by deploying chi-square technique in MapReduce model to handle large amount of agricultural data. The experimental results show that the proposed Chi-Square MapReduce model has high accuracy and less processing time than the existing feature selection methods.

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An Improved Feature Selection and Classification using Decision Tree for Crop Datasets

Cited by 9 publications

References 7 publications

Crop prediction based on soil and environmental characteristics using feature selection techniques

Crop prediction based on soil and environmental characteristics using feature selection techniques

Feature Selection Method based on Fisher’s Exact Test for Agricultural Data

Chi-Square MapReduce Model for Agricultural Data

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