On machine learning technique selection for classification

Kurniawan, Rahmad; Nazri, Mohd Zakree Ahmad; Irsyad, Muhammad

doi:10.1109/iceei.2015.7352559

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…The next process is data pre-processing whereby we eliminate noise or clean the missing data value, inconsistent data and outliers. The data extraction process is challenging due to irrelevant, redundant, noisy, unreliable attribute and unsuitability of a method to obtain unidentified patterns from data may effect to an inaccurate of result [20]. The data preprocessing stage purposes to enhance machine-learning performance.…”

Section: B Pre-processingmentioning

confidence: 99%

Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

Kurniawan

Nazri

Abdullah

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The conventional approach in developing a rule-based expert system usually applies a tedious, lengthy and costly knowledge acquisition process. The acquisition process is known as the bottleneck in developing an expert system. Furthermore, manual knowledge acquisition can eventually lead to erroneous in decision-making and function ineffective when designing any expert system. Another dilemma among knowledge engineers are handing conflict of interest or high variance of inter and intrapersonal decisions among domain experts during knowledge elicitation stage. The aim of this research is to improve the acquisition of knowledge level using a data mining technique. This paper investigates the effectiveness of an association rule mining technique in generating new rules for an expert system. In this paper, FP-Growth is the machine learning technique that was used in acquiring rules from the eye disease diagnosis records collected from Sumatera Eye Center (SMEC) Hospital in Pekanbaru, Riau, Indonesia. The developed systems are tested with 17 cases. The ophthalmologists inspected the results from automatic rule generator for eye diseases diagnosis. We found that the introduction of FP-Growth association rules into the eye disease knowledge-based systems, able to produce acceptable and promising eye diagnosing results approximately 88% of average accuracy rate. Based on the test results, we can conclude that Conjunctivitis and Presbyopia disease are the most dominant suffering in Indonesia. In conclusion, FP-growth association rules are very potential and capable of becoming an adequate automatic rules generator, but still has plenty of room for improvement in the context of eye disease diagnosing.

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Section: B Pre-processingmentioning

confidence: 99%

Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

Kurniawan

Nazri

Abdullah

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

Self Cite

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“…1 and 10000. The normalization will make the value magnitudes scale to appreciably low values ( [21], [22]). In this study, normalization is applied the following attributes: Age, Number of Households, Total Income, Average Monthly Income and Per Capita Income, in which their range become from 0 to 1.…”

Section: ) Data Cleaningmentioning

confidence: 99%

Machine Learning Approach for Bottom 40 Percent Households (B40) Poverty Classification

Sani¹,

Rahman²,

Bakar³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Malaysia citizens are categorised into three different income groups which are the Top 20 Percent (T20), Middle 40 Percent (M40), and Bottom 40 Percent (B40). One of the focus areas in the Eleventh Malaysia Plan (11MP) is to elevate the B40 household group towards the middle-income society. Based on recent studies by the World Bank, Malaysia is expected to enter the high-income economy status no later than the year 2024. Thus, it is essential to clarify the B40 population through a predictive classification as a prerequisite towards developing a comprehensive action plan by the government. This paper is aimed at identifying the best machine learning models using Naive Bayes, Decision Tree and k-Nearest Neighbors algorithm for classifying the B40 population. Several data pre-processing task such as data cleaning, feature engineering, normalisation, feature selection: Correlation Attribute, Information Gain Attribute and Symmetrical Uncertainty Attribute and sampling methods using SMOTE has been conducted to the raw dataset to ensure the quality of the training data. Each classifier is then optimized using different tuning parameter with 10-Fold Cross Validation for achieving the optimal values before the performance of the three classifiers are compared to each other. For the experiments, a dataset from National Poverty Data Bank called eKasih obtained from the Society Wellbeing Department, Implementation Coordination Unit of Prime Minister's Department (ICU JPM), consisting of 99,546 households from 3 different states: Johor, Terengganu and Pahang are used to train each of the machine learning model. The experimental results using 10-Fold Cross-Validation method demonstrates that the overall performance of Decision Tree model outperformed the other models and the significance test specified the result is statistically significance.

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“…Different approaches have been used to classify pedestrian activity states and phone poses in a wide range of contexts and applications. Neural networks (NNs) can perform many tasks, such as classification of patterns, approximation of function, prediction, categorization, time series prediction, and optimization [34,35]. Machine learning (ML) necessitates the manual computation of features for the classifier, which is potentially limited by a user's subject knowledge [36].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

et al. 2023

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These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.

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On machine learning technique selection for classification

Cited by 6 publications

References 21 publications

Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

Machine Learning Approach for Bottom 40 Percent Households (B40) Poverty Classification

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

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