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The monarch butterfly optimization algorithm for solving feature selection problems
Feature selection (FS) is considered to be a hard optimization problem in data mining and
some artificial intelligence fields. It is a process where rather than studying all of the features …
some artificial intelligence fields. It is a process where rather than studying all of the features …
Application and performance of data mining techniques in stock market: A review
Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional
forecasting methods and the pursuit to uncover the hidden patterns in stock market data …
forecasting methods and the pursuit to uncover the hidden patterns in stock market data …
[كتاب][B] Machine Learning for Computer and Cyber Security
BB Gupta, M Sheng - 2019 - api.taylorfrancis.com
Names: Gupta, Brij, 1982-editor.| Sheng, Quan Z. editor. Title: Machine learning for computer
and cyber security: principles, algorithms, and practices/editors Brij B. Gupta, National …
and cyber security: principles, algorithms, and practices/editors Brij B. Gupta, National …
A hybrid mine blast algorithm for feature selection problems
Feature selection (FS) is the process of finding the least possible number of features that are
able to describe a dataset in the same way as the original features. Feature selection is a …
able to describe a dataset in the same way as the original features. Feature selection is a …
Solving feature selection problems by combining mutation and crossover operations with the monarch butterfly optimization algorithm
Feature selection (FS) is used to solve hard optimization problems in artificial intelligence
and data mining. In the FS process, some, rather than all of the features of a dataset are …
and data mining. In the FS process, some, rather than all of the features of a dataset are …
[HTML][HTML] African Buffalo algorithm: training the probabilistic neural network to solve classification problems
Classification is used to categorize data and produce decisions for several domains. To
improve the accuracy of classification, researchers have tended to hybridize the neural …
improve the accuracy of classification, researchers have tended to hybridize the neural …
[PDF][PDF] Flower pollination algorithm for solving classification problems
M Alweshah, MA Qadoura, AI Hammouri… - Int J Adv Soft Comput …, 2020 - 188.247.81.52
Classification remains as a most significant area in data mining. Probabilistic Neural
Network (PNN) is repeatedly used for classification problems. The main aims of this paper …
Network (PNN) is repeatedly used for classification problems. The main aims of this paper …
Improved water cycle algorithm with probabilistic neural network to solve classification problems
Classification is achieved through the categorisation of objects into predefined categories or
classes, where the categories or classes are created based on a similar set of attributes of …
classes, where the categories or classes are created based on a similar set of attributes of …
Metaheuristic algorithms-based feature selection approach for intrusion detection
Since data mining is intended to extract the required knowledge from very large amounts of
data, the presence of irrelevant or redundant attributes leads to poor predictability and …
data, the presence of irrelevant or redundant attributes leads to poor predictability and …
-Hill climbing algorithm with probabilistic neural network for classification problems
Classification is a crucial step in the data mining field. The probabilistic neural network
(PNN) is an efficient method developed for classification problems. The success factor of …
(PNN) is an efficient method developed for classification problems. The success factor of …