A generalized weighted distance k-Nearest Neighbor for multi-label problems

Rastin, Niloofar; Jahromi, Mansoor Zolghadri; Taheri, Mohammad

doi:10.1016/j.patcog.2020.107526

Cited by 34 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, the map-reduce based distributed and parallel machine learning algorithms are the focus of the research community. The MapReduce framework has emerged as a powerful, robust, and distributed parallel programming model [22][23][24][25] provides a solution with good performance and efficient execution to large-scale data analytic applications including data mining, web page access ranking, graph analysis, image classification and bioinformatics [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. Kolb et al [45] investigated the use of the MapReduce programming model for parallel entity resolution with automated data partitioning on real-world datasets.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

Moorthy¹,

Sv²,

Aa³

et al. 2021

Preprint

View full text Add to dashboard Cite

With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3–4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications.

show abstract

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

Moorthy¹,

Sv²,

Aa³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The nearest neighbor (NN) [48] rule is a nonparametric method for classification based on instances. Taking into account the problem in Figure 2, a product in the MDD-DS p follows the definition {p 1 , p 2 , .…”

Section: Nearest-neighbors Modelmentioning

confidence: 99%

Classification of Retail Products: From Probabilistic Ranking to Neural Networks

et al. 2021

View full text Add to dashboard Cite

Food retailing is now on an accelerated path to a success penetration into the digital market by new ways of value creation at all stages of the consumer decision process. One of the most important imperatives in this path is the availability of quality data to feed all the process in digital transformation. However, the quality of data are not so obvious if we consider the variety of products and suppliers in the grocery market. Within this context of digital transformation of grocery industry, Midiadia is a Spanish data provider company that works on converting data from the retailers’ products into knowledge with attributes and insights from the product labels that is maintaining quality data in a dynamic market with a high dispersion of products. Currently, they manually categorize products (groceries) according to the information extracted directly (text processing) from the product labelling and packaging. This paper introduces a solution to automatically categorize the constantly changing product catalogue into a 3-level food taxonomy. Our proposal studies three different approaches: a score-based ranking method, traditional machine learning algorithms, and deep neural networks. Thus, we provide four different classifiers that support a more efficient and less error-prone maintenance of groceries catalogues, the main asset of the company. Finally, we have compared the performance of these three alternatives, concluding that traditional machine learning algorithms perform better, but closely followed by the score-based approach.

show abstract

“…Algorithm adaptation methods adapt the existing single label classification algorithms to multi-label data [9]. For example, Rastin et al [36] proposed a prototype weighting method to adapt the distance measure based on the ML-kNN method [12], the prototype weights were adjusted by gradient ascent method in order to maximize the objective function as macro-F1 measure. Kouchaki et al [37] designed the multilabel random forest (MLRF) models for treating tuberculosis resistance classification and mutation ranking in medical.…”

Section: Related Workmentioning

confidence: 99%

Representation Learning With Dual Autoencoder for Multi-Label Classification

Zhu

Yang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label data. The crucial problem of multi-label classification is the more robust and higher-level feature representation learning, which can reduce non-helpful feature attributes from the input space prior to training. In recent years, deep learning methods based on autoencoders have achieved excellent performance in multi-label classification for the advantages of powerful representations learning ability and fast convergence speed. However, most existing autoencoder-based methods only rely on the single autoencoder model, which pose challenges for multi-label feature representations learning and fail to measure similarities between data spaces. To address this problem, in this paper, we propose a novel representation learning method with dual autoencoder for multi-label classification. Compared to the existing autoencoder-based methods, our proposed method can capture different characteristics and more abstract features from data by the serially connection of two different types of autoencoders. More specifically, firstly, the algorithm of Reconstruction Independent Component Analysis (RICA) in sparse autoencoder is trained on patches on all training and test dataset for robust global feature representations learning. Secondly, with the output of RICA, stacked autoencoder with manifold regularization (SAMR) is introduced to ameliorate the quality of multi-label features learning. Comprehensive experiments on several real-world data sets demonstrate the effectiveness of our proposed approach compared with several competing state-of-the-art methods.

show abstract

A generalized weighted distance k-Nearest Neighbor for multi-label problems

Cited by 34 publications

References 27 publications

WITHDRAWN: Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

WITHDRAWN: Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

Classification of Retail Products: From Probabilistic Ranking to Neural Networks

Representation Learning With Dual Autoencoder for Multi-Label Classification

Contact Info

Product

Resources

About