Multiclass Model for Agriculture development using Multivariate Statistical method

• URL: http://arxiv.org/abs/2009.05783v2
• Date: Wed, 7 Oct 2020 13:53:21 GMT
• Title: Multiclass Model for Agriculture development using Multivariate Statistical method
• Authors: N Deepa, Mohammad Zubair Khan, Prabadevi B, Durai Raj Vincent P M, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu
• Abstract summary: The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate.
• Score: 2.1530718840070784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

Related papers

• Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification [3.1850615666574806]
  This study investigates how consistent metrics are at evaluating different models under different data scenarios. I find that for binary classification tasks, evaluation metrics that are less influenced by prevalence offer more consistent ranking of a set of different models.
  arXiv  Detail & Related papers  (2024-08-19T17:52:38Z)
• Predictive Analytics of Varieties of Potatoes [2.336821989135698]
  We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties.
  arXiv  Detail & Related papers  (2024-04-04T00:49:05Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• On the Strong Correlation Between Model Invariance and Generalization [54.812786542023325]
  Generalization captures a model's ability to classify unseen data. Invariance measures consistency of model predictions on transformations of the data. From a dataset-centric view, we find a certain model's accuracy and invariance linearly correlated on different test sets.
  arXiv  Detail & Related papers  (2022-07-14T17:08:25Z)
• Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties [62.997667081978825]
  In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
  arXiv  Detail & Related papers  (2021-12-15T18:56:39Z)
• Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization [0.5735035463793008]
  Average categorization accuracy is often used in isolation. As the number of classes increases, the amount of information conveyed by average accuracy alone dwindles. While its most glaring weakness is its failure to describe the model's performance on a class-by-class basis, average accuracy also fails to describe how performance may vary from one trained model of the same architecture, to another.
  arXiv  Detail & Related papers  (2021-09-07T15:47:27Z)
• Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions [79.35722941720734]
  Generalised linear models for multi-class classification problems are one of the fundamental building blocks of modern machine learning tasks. We prove exacts characterising the estimator in high-dimensions via empirical risk minimisation. We discuss how our theory can be applied beyond the scope of synthetic data.
  arXiv  Detail & Related papers  (2021-06-07T16:53:56Z)
• Mycorrhiza: Genotype Assignment usingPhylogenetic Networks [2.286041284499166]
  We introduce Mycorrhiza, a machine learning approach for the genotype assignment problem. Our algorithm makes use of phylogenetic networks to engineer features that encode the evolutionary relationships among samples. Mycorrhiza yields particularly significant gains on datasets with a large average fixation index (FST) or deviation from the Hardy-Weinberg equilibrium.
  arXiv  Detail & Related papers  (2020-10-14T02:36:27Z)
• LOGAN: Local Group Bias Detection by Clustering [86.38331353310114]
  We argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. We propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region.
  arXiv  Detail & Related papers  (2020-10-06T16:42:51Z)
• A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One [0.3553493344868413]
  We show that the entire class of unified skew-normal (SUN) distributions is conjugate to several multinomial probit models. We improve upon state-of-the-art solutions for posterior inference and classification.
  arXiv  Detail & Related papers  (2020-07-14T10:08:23Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.