Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

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

doi:10.52549/ijeei.v8i1.1423

Abstract

Pruning is the popular framework for preventing the dilemma of overfitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-AntMiner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers.

Highlights

Machine learning (ML) is a data analysis technique and is intensively used for automating analytical model construction
A new parameter, called importance rate (IR) (ζ), is introduced to control the relative importance to the terms included in the construction rule
This method can effectively guide our classifier in selecting a better parameter value to select the terms included in the construction rule

Summary

Introduction

Machine learning (ML) is a data analysis technique and is intensively used for automating analytical model construction. ML performs a particular task by utilizing discovered patterns and inference without using explicit instructions. This technique is considered a branch of artificial intelligence. ML uses algorithms and statistical models to create a mathematical model on the basis of a given dataset. This model is known as a training model and will be used to make decisions or predictions for real-world problems [1]. Classification is one of the studies in the field of ML with a wide variety of industrial and commercial applications. These applications include medical diagnosis, detection of spam emails, determination of bankruptcy, detection of intrusion, determination of handwritten digits, and face detection [7]–[10]

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