System: Instructions: Compose a comprehensive reply to the query using the search results given. Cite each reference using [Page Number] notation (every result has this number at the beginning).Citation should be done at the end of each sentence. If the search results mention multiple subjects with the same name, create separate answers for each. Only include information found in the results and don't add any additional information. Make sure the answer is correct and don't output false content. Ignore outlier search results which has nothing to do with the question. You can give additional information using you own knowledge but you have to start by the tag [Own knowledge]. Only answer what is asked. The answer should be short and concise and contain equations if possible. Answer step-by-step.

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[Page no. 10] "algorithms for the QoS management in an autonomic IoT platform. Service Oriented Computing and Applications 13, 199–219. Morales, L., Aguilar, J., Garc´es-Jim´enez, A., Gutierrez, De Mesa J., Gomez-Pulido, J., 2020. Advanced fuzzy-logic-based context-driven control for HVAC management systems in buildings. IEEE Access 8, 16111–16126. Mulan. Mulan: A Java Library for Multi-Label Learning. https://mulan.sourceforge.net/ datasets-mlc.html. neuraldesigner Explainable AI Platform. Available from: https://www.neuraldesigner. com/Kaggle. http://kaggle.com. OpenML. A worldwide machine learning lab. https://www.openml.org/. Ouali, Y., Hudelot, C., Tami, M., 2020. An overview of deep semi-supervised learning. arXiv:, 2006, 05278. Paul, J., Jacob, J., Mahmud, M., Vaka, M., Krishnan, S.G., Arifutzzaman, A., Thesiya, D., Xiong, V., Kadirgama, K., Selvaraj, J., 2024. A data mining approach to analyze the role of biomacromolecules-based nanocomposites in sustainable packaging. Int. J. Biol. Macromol. 265, 130850. Quintero, C., Aguilar, J., 2022. LAMDA-HSCC, a semi-supervised learning algorithm based on the multivariate data analysis. Expert System with applications 202. Ray, S., 2019. A quick review of machine learning algorithms. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, pp. 35–39. Rezgui, A., Ben, N’cir Ch, Essoussi, N., 2014. Overlapping clustering with outliers detection. In: International Conference on Pattern Recognition Applications and Methods, pp. 279–286. Ruiz, F., Isaza, C., Agudelo, A., Agudelo, J., 2017. A new criterion to validate and improve the classification process of LAMDA algorithm applied to diesel engines. Eng. Appl. Artif. Intell. 60, 117–127. Sawant, S., Prabukumar, M., 2021. A review on graph-based semi-supervised learning methods for hyperspectral image classification. The Egyptian Journal of Remote Sensing and Space Science 23 (2), 243–248. Sen, P., Hajra, M., Ghosh, M., 2020. Supervised classification algorithms in machine learning: a survey and review. In: Emerging Technology in Modelling and Graphics. Springer, pp. 99–111. Sinaga, K., Yang, M., 2020. Unsupervised K-means clustering algorithm. IEEE Access 8, 80716–80727. Song, Z., Yang,"

[Page no. 1] "A semi-supervised learning algorithm for multi-label classification and multi-assignment clustering problems based on a Multivariate Data Analysis Carlos Quintero Gull a, Jose Aguilar b,c,d,* a Dpto Cs Aplicadas y Humanísticas, Universidad de Los Andes, M´erida, Venezuela b CEMISID, Facultad de Ingeniería, Universidad de Los Andes, M´erida, Venezuela c GIDITIC, Universidad, EAFIT, Medellín, Colombia d IMDEA Networks Institute, Legan´es, Madrid, Spain A R T I C L E I N F O Keywords: Semi-supervised learning algorithm Multi-label classification Multi-assignment clustering Machine learning A B S T R A C T Classically, in classification and clustering problems, an individual is exclusively assigned to a class (labeled data) or a cluster (unlabeled data). However, the assignment to a single class or cluster may be too strict in several real-world domains. In many cases, an individual may belong to multiple classes or clusters at the same time. On the other hand, we are starting to see machine learning algorithms that solve the problem of multi-label classification and multiple cluster assignment, but there are no algorithms that solve both problems simulta- neously. Classic semi-supervised algorithms can work with labeled and unlabeled data simultaneously, but these types of algorithms assign individuals to a single class or a single cluster. Today, artificial intelligence faces the challenge of developing semi-supervised learning algorithms to work with semi-labeled data that can be assigned to different classes or clusters at the same time. In particular, this paper proposes a semi-supervised learning algorithm to fill this gap, which can solve the problems of multi-label classification and multiple cluster assignment simultaneously, on a semi-labeled dataset. This proposal is based on the LAMDA algorithm (Learning Algorithm for Multivariate Data Analysis), which calculates the degree of membership of a data to a group/class. In this work, a membership threshold is defined, which allows individuals to"

[Page no. 4] "between EI and GADNICEI : index = max ( HADEIO, GADNICEI ) (11) The output of the LAMDA-HAD algorithm is the class to which the individual is allocated. Table 1 presents the LAMDA-HAD Algorithm. 3.2. LAMDA –RD LAMDA-RD has been defined by (Morales and Aguilar, 2020) to enhance LAMDA in clustering tasks due to LAMDA tends to generate numerous groups. To solve this problem (Morales and Aguilar, 2020), determined a new measure of grouping, and additionally, developed a procedure for automatic group merger. The concepts of LAMDA-RD are defined below. Definition 8. Cauchy Marginal Adequacy Degree (CMAD). It de- fines the Marginal Adequacy Degree based on the Function of Fuzzy Cauchy: CMADk,j ( xj ⃒⃒ρkj ) = 1 1 + dist ( xj, ρkj ) (12) Where: dist ( xj, ρkj ) is the length between the feature j of the data X and the variable j of the centroid of the group kρkj. Definition 9. Robust Marginal Adequacy Degree (RMAD). It re- defines the MAD by multiplying it by a penalty factor Kk,X calculated for each group k: RMADk,j ( xj ⃒⃒ρkj ) = KkX × CMADkj ( xj ⃒⃒ρkj ) (13) Two parameters are required to determine kkX. The first is the mean distance of the individual between the groups, which is: dkX = 1 n ∑ j=n j=1 ⃒⃒xj − ρkj ⃒⃒ (14) The second is the mean distance between neighbor groups (dnb; ∈ [0, 1]), which is a parameter that is defined via calibration. Definition 10. Penalty factor (KkX). It is computed using Equation (15) when the distance dkX is greater than dnb: kkX = dnb dnb + dist(dkX, dnb) (15) According to Eq. (15), the distance dk,X penalizes the unlikeness between the observations and the groups, and is named Robust Distance C.Q. Gull and J."

[Page no. 10] "of linguistic descriptors of concepts. In: Approximate Reasoning in Decision Analysis. North-Holland, pp. 165–175. Alonso, J., Paredes, R., Rosso, P., 2017. Feature representation for social circles detection using MAC. Neural Comput. Appl. 28 (9), 2395–2402. Bedoya, C., Villanova, J., Narvaez, C., 2014. Yager–Rybalov Triple Π operator as a means of reducing the number of generated clusters in unsupervised anuran vocalization recognition. In: Proceeding Mexican International Conference on Artificial Intelligence, pp. 382–391. Botía, J., Botía, D., 2018. On LAMDA clustering method based on typicality degree and intuitionistic fuzzy sets. Expert Syst. Appl. 107, 196–221. Carvalho, T., Soares, F., Vita, R., Francisco, R., Basto, J., Alcal´a, S., 2019. A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024. Cerrada, M., Aguilar, J., Altamiranda, J., S´anchez, R., 2019. A hybrid heuristic algorithm for evolving models in simultaneous scenarios of classification and clustering. Knowl. Inf. Syst. 61 (2), 755–798. Chong, Y., Ding, Y., Yan, Q., Pan, 2020. Graph-based semi-supervised learning: a review. Neurocomputing 408, 216–230. Cui, J., Ding, Z., Fan, P., Al-Dhahir, N., 2018. Unsupervised machine learning-based user clustering in millimeter-wave-NOMA systems. IEEE Trans. Wireless Commun. 17 (11), 7425–7440. F¨anti, P., Sieranoja, S., 2018. K-means properties on six clustering benchmark datasets. Appl. Intell. 48 (12), 4743–4759. http://cs.joensuu.fi/sipu/datasets/. Garrido, L., Serrano, M., Maudes, R., Rodríguez, García, O., 2024. Ensemble methods and semi-supervised learning for information fusion: a review and future research irections. Inf. Fusion, 102310. Han, K., Sheng, V., Song, Y., Liu, Y., Qiu, C., Ma, S., Liu, Z., 2024. Deep semi-supervised learning for medical image segmentation: a review. Expert Syst. Appl., 123052 Huang, Z., Xue, C., Han, B., Jian, C. Chen G., 2021. Universal semi-supervised learning. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S., Wortman Vaughan, J. (Eds.), Advances in Neural Information Processing Systems. Curran Associates,"

[Page no. 7] "centroid of the cluster i in the descriptor j, xj is the average of all values of the dataset in the descriptor j, k are the clusters formed. Table 2 LAMDA-RD algorithm. Input X = [ x1; x2; . . . ; xj ; . . . ; xn ] Procedure 1 Normalize the variables of the data using Eq. (1). 2 .Set the values dnb and dt (by calibration) 3 .Calculate the CMADk,j for each descriptor (using Eq (12)) 4 .Calculate the RMADk,j using Eq. (13). 5 .Calculate GADx,k (using Eq (16)) 6 .Identify the group with max(GAD x1,GAD x k,….,GAD x m,GAD x NIC ). 7 . If max(GADkX ) > GADNICX, then allocate X to a group update ρkj conserve current m else define a new group m = m + 1 8 Call merging process End Output: Cluster Index Table 3 LAMDA-SeMLACA algorithm. Input X = [ x1; x2; . . . ; xj ; . . . ; xn ] Procedure 1 Normalize the variables of the data using Eq. (1). 2 Run LAMD -HAD until step 5 (see Table 1) 3 Run LAMDA-RD until step 5 (see Table 2) 4 Establish the indexes of the data with Eq. (30). End Output: Category or cluster Indexes C.Q. Gull and J. Aguilar Engineering Applications of Arti�cial Intelligence 137 (2024) 109189 6  S2 w is the variance within a cluster, defined by: S2 w = ∑ k i=1 ( xi j − xi n,j )2 k − 1 (37) Where, xi j is the centroid of the group i in the descriptor j, xi n,j is the value of the individual n in the group i and descriptor j. The range of values of (ρ) is zero to one. Small values of (ρ) imply that the"

Query: Descibe LAMDA –RD algorithm 
Answer:
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