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44 results on '"Mansouri, Najme"'

17. Text Mining using Nonnegative Matrix Factorization and Latent Semantic Analysis

Author

Hassani, Ali, Iranmanesh, Amir, and Mansouri, Najme

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Text clustering is arguably one of the most important topics in modern data mining. Nevertheless, text data require tokenization which usually yields a very large and highly sparse term-document matrix, which is usually difficult to process using conventional machine learning algorithms. Methods such as Latent Semantic Analysis have helped mitigate this issue, but are nevertheless not completely stable in practice. As a result, we propose a new feature agglomeration method based on Nonnegative Matrix Factorization, which is employed to separate the terms into groups, and then each group's term vectors are agglomerated into a new feature vector. Together, these feature vectors create a new feature space much more suitable for clustering. In addition, we propose a new deterministic initialization for spherical K-Means, which proves very useful for this specific type of data. In order to evaluate the proposed method, we compare it to some of the latest research done in this field, as well as some of the most practiced methods. In our experiments, we conclude that the proposed method either significantly improves clustering performance, or maintains the performance of other methods, while improving stability in results.

Published
2019

30. Handwriting Recognition Algorithm in Different Languages: Survey

Author

Mirvaziri, Hamid, Masood Javidi, Mohammad, Mansouri, Najme, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Badioze Zaman, Halimah, editor, Robinson, Peter, editor, Petrou, Maria, editor, Olivier, Patrick, editor, Schröder, Heiko, editor, and Shih, Timothy K., editor

Published
2009
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35. A Novel Correlation-Based Feature Selection Approach using Manta Ray Foraging Optimization.

Author

Mansouri, Najme and Shiri, Mohammad Ansari

Subjects
FEATURE selection, MACHINE learning, OPTIMIZATION algorithms, ACCURACY, DATA mining
Abstract

Recent advances in science, engineering, and technology have created massive datasets. As a result, machine learning and data mining techniques cannot perform well on these huge datasets, because they contain redundant, noisy, and irrelevant features. The purpose of feature selection is reducing the dimensionality of datasets by selecting the most relevant attributes while simultaneously increasing classification accuracy. The application of meta- heuristic optimization techniques has become increasingly popular for feature selection in recent years due to their ability to overcome the limitations of traditional optimization methods. This paper presents a binary version of the Manta Ray Foraging Optimizer (MRFO), an alternative optimization algorithm. Besides reducing costs and reducing calculation time, we also incorporated Spearman's correlation coefficient into the proposed method, which we called Correlation Based Binary Manta Ray Foraging (CBBMRF). It eliminates highly positive correlation features at the beginning of the calculation, avoiding additional calculations and leading to faster subset selection. A comparison is made between the presented algorithms and five state-of-the-art meta-heuristics using 10 standard UCI datasets. As a result, the proposed algorithms demonstrate superior performance when solving feature selection problems. [ABSTRACT FROM AUTHOR]

Published
2023
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