Classification of IRIS Dataset using Classification Based KNN Algorithm in Supervised Learning

Thirunavukkarasu, K.; Singh, Ajay; Rai, Prakhar; Gupta, Sachin

doi:10.1109/ccaa.2018.8777643

Cited by 29 publications

(16 citation statements)

References 1 publication

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“…Out of the three distance metrics used, Manhattan was found to give higher performance followed by Chebyshev and then the Euclidean distance metric. Thirunavukkarasu K et al [6] have classified the iris dataset using a kNN classifier.…”

Section: Literature Surveymentioning

confidence: 99%

Study of distance metrics on k - nearest neighbor algorithm for star categorization

Nayak

Bhat

Reddy

et al. 2022

J. Phys.: Conf. Ser.

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Classification of stars is essential to investigate the characteristics and behavior of stars. Performing classifications manually is error-prone and time-consuming. Machine learning provides a computerized solution to handle huge volumes of data with minimal human input. k-Nearest Neighbor (kNN) is one of the simplest supervised learning approaches in machine learning. This paper aims at studying and analyzing the performance of the kNN algorithm on the star dataset. In this paper, we have analyzed the accuracy of the kNN algorithm by considering various distance metrics and the range of k values. Minkowski, Euclidean, Manhattan, Chebyshev, Cosine, Jaccard, and Hamming distance were applied on kNN classifiers for different k values. It is observed that Cosine distance works better than the other distance metrics on star categorization.

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Section: Literature Surveymentioning

confidence: 99%

Study of distance metrics on k - nearest neighbor algorithm for star categorization

Nayak

Bhat

Reddy

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The best performances of Dempster’s method [ 15 ], Murphy’s method [ 35 ], Xiao’s method [ 59 ], Chen’s method [ 60 ] and the proposed method were tested on the classification task of the iris data set. In addition to the above methods based on evidence theory, the KNN-based method [ 61 ] and deep neural network-based method [ 62 ] were also involved in the comparison, and the results are shown in Table 12 . The proposed method was able to achieve a maximum accuracy of 97.04%, which is higher than the other four algorithms that participated in the comparison.…”

Section: Comparative Analysismentioning

confidence: 99%

Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy

Chen

Zhong

Tang

et al. 2022

Entropy

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Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source information, in this paper, we propose a multi-source information fusion method based on the Dempster-Shafer (DS) evidence theory with the negation of reconstructed basic probability assignments (nrBPA). To determine the initial basic probability assignment (BPA), the Gaussian distribution BPA functions with padding terms are used. After that, nrBPAs are determined by two processes, reassigning the high blur degree BPA and transforming them into the form of negation. In addition, evidence of preliminary fusion is obtained using the entropy weight method based on the improved belief entropy of nrBPAs. The final fusion results are calculated from the preliminary fused evidence through the Dempster’s combination rule. In the experimental section, the UCI iris data set and the wine data set are used for validating the arithmetic processes of the proposed method. In the comparative analysis, the effectiveness of the BPA determination using a padded Gaussian function is verified by discussing the classification task with the iris data set. Subsequently, the comparison with other methods using the cross-validation method proves that the proposed method is robust. Notably, the classification accuracy of the iris data set using the proposed method can reach an accuracy of 97.04%, which is higher than many other methods.

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“…The Iris classification problem was used in [21]. The Iris dataset contains 4 features (length and width of sepals and petals) of 3 species of Iris (Iris setosa, Iris virginica, and Iris versicolor) as shown in [22,23]. The data contains 150 samples.…”

Section: A Practical Iris Classification Problemmentioning

confidence: 99%

Levy Enhanced Cross Entropy-based Optimized Training of Feedforward Neural Networks

Pandya

Dabhi

Mochi

et al. 2022

Eng. Technol. Appl. Sci. Res.

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An Artificial Neural Network (ANN) is one of the most powerful tools to predict the behavior of a system with unforeseen data. The feedforward neural network is the simplest, yet most efficient topology that is widely used in computer industries. Training of feedforward ANNs is an integral part of an ANN-based system. Typically an ANN system has inherent non-linearity with multiple parameters like weights and biases that must be optimized simultaneously. To solve such a complex optimization problem, this paper proposes the Levy Enhanced Cross Entropy (LE-CE) method. It is a population-based meta-heuristic method. In each iteration, this method produces a "distribution" of prospective solutions and updates it by updating the parameters of the distribution to obtain the optimal solutions, unlike traditional meta-heuristic methods. As a result, it reduces the chances of getting trapped into local minima, which is the typical drawback of any AI method. To further improve the global exploration capability of the CE method, it is subjected to the Levy flight which consists of a large step length during intermediate iterations. The performance of the LE-CE method is compared with state-of-the-art optimization methods. The result shows the superiority of LE-CE. The statistical ANOVA test confirms that the proposed LE-CE is statistically superior to other algorithms.

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Classification of IRIS Dataset using Classification Based KNN Algorithm in Supervised Learning

Cited by 29 publications

References 1 publication

Study of distance metrics on k - nearest neighbor algorithm for star categorization

Study of distance metrics on k - nearest neighbor algorithm for star categorization

Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy

Levy Enhanced Cross Entropy-based Optimized Training of Feedforward Neural Networks

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