Learning ASsociations by Self-Organization: The LASSO model

Midenet, Sophie; Grumbach, Alain

doi:10.1016/0925-2312(94)90069-8

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…Midenet and Grumbach [20] observed that during self-organisation, some input dimensions play a dominant role and drive the map organisation. The coding scheme of the input vectors (described in Section 4.4) enables us to influence the selective treatment performed by the map among input dimensions.…”

Section: Input Coding Influencementioning

confidence: 99%

“…Concerning the coding of the variables, we used previous experience with the model and adopted the following coding scheme [20]. The numerical variables are normalised; each is represented by one input unit.…”

Section: Experimental Conditions For Map Designmentioning

confidence: 99%

“…The LASSO model (Learning Associations by Self-Organisation) [20] consists of associating output vectors to input vectors through a map, after self-organising it on the basis of both input and desired output vectors given altogether. The LASSO model had been tested on pattern recognition tasks [20,21].…”

Section: Som Use For Imputation Of Missing Datamentioning

confidence: 99%

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Self-Organising Map for Data Imputation and Correction in Surveys

Fessant¹,

Midenet²

2002

Neural Computing & Applications

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This paper is dedicated to erroneous data detection and imputation methods in surveys. We describe experiments conducted under the scope of a European project for studying new statistical methods based on neural networks. We show that the selforganising map can be used successfully for these tasks. A self-organising map is calibrated according to the available observations, described through a set of correlated variables handled together. The map can then be used both to detect erroneous data and to impute values to partial observations. We apply these principles to a real size transport survey database. We show that the performance of our imputation model compares well to other classical methods, and that the use of a self-organising map for data correction provides a performing system for data validation, data correction and data analysis.

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Section: Input Coding Influencementioning

confidence: 99%

Section: Experimental Conditions For Map Designmentioning

confidence: 99%

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Self-Organising Map for Data Imputation and Correction in Surveys

Fessant¹,

Midenet²

2002

Neural Computing & Applications

Self Cite

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“…The LASSO model (Learning ASsociations by Self-Organization) [27] was designed for supervised learning based on unsupervised SOM model. The main idea behind this model is that output patterns can be presented to the SOM map for its organization simultaneously with input patterns.…”

Section: Lasso Modelmentioning

confidence: 99%

Modifications of Unsupervised Neural Networks for Single Trial P300 Detection

Vařeka¹,

Mautner²

2018

NNW

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P300 brain-computer interfaces (BCIs) have been gaining attention in recent years. To achieve good performance and accuracy, it is necessary to optimize both feature extraction and classification algorithms. This article aims at verifying whether supervised learning models based on self-organizing maps (SOM) or adaptive resonance theory (ART) can be useful for this task. For feature extraction, the state-of-the-art Windowed means paradigm was used. For classification, proposed classifiers were compared with state-of-the-art classifiers used in BCI research, such as Bayesian Linear Discriminant Analysis, or shrinkage LDA. Publicly available datasets from fifteen healthy subjects were used for the experiments. The results indicated that SOM-based models yield better results than ART-based models. The best performance was achieved by the LASSO model that was comparable to state-of-the-art BCI classifiers. Further possibilities for improvements are discussed.

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“…The SOM is an artificial neural network model that maps high-dimensional input data space onto usually two-dimensional lattice of neurons in an unsupervised way. Although, the SOM is an originally unsupervised algorithm there exist supervised extensions [9], [14], [13], [7]. The SOM has been proved as an efficient data mining tool in many real life applications [11], [12], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Visualizing Random Forest with Self-Organising Map

Płoński

Заремба

2014

Artificial Intelligence and Soft Computing

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Abstract. Random Forest (RF) is a powerful ensemble method for classification and regression tasks. It consists of decision trees set. Although, a single tree is well interpretable for human, the ensemble of trees is a black-box model. The popular technique to look inside the RF model is to visualize a RF proximity matrix obtained on data samples with Multidimensional Scaling (MDS) method. Herein, we present a novel method based on Self-Organising Maps (SOM) for revealing intrinsic relationships in data that lay inside the RF used for classification tasks. We propose an algorithm to learn the SOM with the proximity matrix obtained from the RF. The visualization of RF proximity matrix with MDS and SOM is compared. What is more, the SOM learned with the RF proximity matrix has better classification accuracy in comparison to SOM learned with Euclidean distance. Presented approach enables better understanding of the RF and additionally improves accuracy of the SOM.

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Learning ASsociations by Self-Organization: The LASSO model

Cited by 16 publications

References 17 publications

Self-Organising Map for Data Imputation and Correction in Surveys

Self-Organising Map for Data Imputation and Correction in Surveys

Modifications of Unsupervised Neural Networks for Single Trial P300 Detection

Visualizing Random Forest with Self-Organising Map

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