Rule pruning techniques in the ant-miner classification algorithm and its variants: A review

Al-Behadili, Hayder Naser Khraibet; Ku-Mahamud, Ku Ruhana; Sagban, Rafid

doi:10.1109/iscaie.2018.8405448

Cited by 21 publications

(13 citation statements)

References 39 publications

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“…The proposed algorithm relies on only the pheromone which means that no heuristic information is required to construct the clustering solution. To construct a route of attributes, a centroid highly similar to the ant-Miner for the classification problem was employed (Al-Behadili et al, 2019;2018b) Given the use of an example of a data set, in which the number of attribute D is three (3), each centroid contains three attributes. The number of clusters K involves three (3) centroids, in which each single cluster has one single centroid.…”

Section: Methodsmentioning

confidence: 99%

Modified ACS Centroid Memory for Data Clustering

Jabbar¹,

Ku-Mahamud²,

Sagban³

2019

Journal of Computer Science

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Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results. The algorithm's memory records previous search regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration. Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems. However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant's movements in the pheromone matrix. The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable. The label of the current centroid value changes from one iteration to another because of changes in centroid label. Thus the pheromone values are lost because they are associated with the label (position) of the centroid. ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run. This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution. This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid. Centroids will be identified based on the adaptive instance route. A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.

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Section: Methodsmentioning

confidence: 99%

Modified ACS Centroid Memory for Data Clustering

Jabbar¹,

Ku-Mahamud²,

Sagban³

2019

Journal of Computer Science

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“…| | is the number of instances in dataset , and | |is the number of instances in which is the dataset partitioned by the attribute that have different value. Information gain, which measures the homogeneity difference of data before and after a node is split by attribute , is given by equation (2)…”

Section: Decision Treementioning

confidence: 99%

“…Training error can be reduced by increasing data complexity, but it can increasing testing error which causes it cannot predict new data properly. This condition is called overfitting which can occur due to noise or the lack of a representative sample in the training dataset [2].…”

Section: Introductionmentioning

confidence: 99%

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Bayes Risk Post-Pruning in Decision Tree to Overcome Overfitting Problem on Customer Churn Classification

Christianti¹,

Abdullah²,

Nurrohmah³

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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Classification is the process of assigning a set of data into an existing class. Decision tree is claimed to be faster and produces better accuracy compared to another classifier. However, it has some drawbacks in which the classifier is susceptible to overfitting. This problem can be avoided by post-pruning that trimming the small influence subtree in conducting the classification to improve model performance in predicting data. This paper proposes a Post-Pruning method by applying Bayes Risk, in which the risk estimation of each parent node compared with its leaf. This method is applied to two datasets of customer churn classification from the Kaggle site and IBM Datasets with three different sizes for training dataset (60%, 70%, and 80%). For the result, Bayes Risk Post-Pruning can improve decision tree performance and the larger the size of the training dataset was associated with higher accuracy, precision, and recall of the model.

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“…This cooperative behavior and other unique features prove that ACO is suitable in building a new algorithm. Many NP-complete problems such as the travelling salesman problem (Brezina & Čičková, 2011), fault tolerance (Bukhari, Ku-Mahamud, & Morino, 2017), sequential ordering (Skinderowicz, 2015), grid scheduling (Ku-Mahamud, Din, & Nasir, 2011), and data classification (Al-Behadili, Ku-Mahamud, & Sagban, 2018) have been solved using ACO algorithms. ACO has also been applied to solve routing problems in WSNs because it is suitable to be implemented in static, mobile, and dynamic WSN environments.…”

Section: Aco Work In Wireless Sensor Networkmentioning

confidence: 99%