Classification of Multivariate Data Sets without Missing Values Using Memory Based Classifiers - An Effectiveness Evaluation

Devasena, C. Lakshmi

doi:10.5121/ijaia.2013.4110

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The mean Receive times were fairly consistent, but showed a downward trend (R1=24.00, R2=18.35, R3=15.95). The user satisfaction results obtained from using the PSSUQ were classified into the following categories (Devasena, 2013;Markham, 2014;Aviv et al, 2012):…”

Section: Resultsmentioning

confidence: 99%

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Designing a Natural User Interface to Support Information Sharing among Co-Located Mobile Devices

Son¹,

Wesson²,

Vogts³

2018

SACJ

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Users of mobile devices share their information through various methods, which are supported by mobile devices. However, the information sharing process of these methods are typically redundant and sometimes tedious. This is because it may require the user to repeatedly perform a series of steps to share one or more selected files with another individual. The proliferation of mobile devices support new, more intuitive, and less complicated solutions to information sharing in the field of mobile computing. The aim of this paper is to present MotionShare, which is a NUI application that supports information sharing among co-located mobile devices. Unlike other existing systems, MotionShare’s distinguishing attribute is its inability of relying on additional and assisting technologies in determining the positions of devices. A primary example is using an external camera to determine device positioning in a spatial environment. An analytical evaluation investigated the accuracy of device positioning and gesture recognition, where the results were positive. The empirical evaluation investigated any usability issues. The results of the empirical evaluation showed high levels of user satisfaction and that participants preferred touch gestures to point gestures.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, the IB1 classifier was selected and used within MotionShare. The IB1 algorithm is an instance-based nearest neighbour classifier (Devasena, 2013). It uses normalised Euclidean distance to determine the training instance closest to the given test instance, and predicts the same class as this training instance.…”

Section: Latencymentioning

confidence: 99%

Designing a Natural User Interface to Support Information Sharing among Co-Located Mobile Devices

Son¹,

Wesson²,

Vogts³

2018

SACJ

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“…Other than that, the value of 'k' for nearest neighbor can be different. This determines how many cases must be considered as neighbor to decide how to classify an unknown instance [3]. Moreover, the k-Nearest Neighbor was also used in classifying drinking water quality (DWQ) [18].…”

Section: Related Workmentioning

confidence: 99%

“…Classification is a machine learning method that is used in predict data instances [2]. It is also one of the data mining tasks, considering that many of the classification problems do not occur in only one application areas [3]. Data mining Classification maps a data item into one of several predefined categorical classes.…”

Section: Introductionmentioning

confidence: 99%

The Study on the Accuracy of Classifiers for Water Quality Application

Rosly¹,

Makhtar²,

Awang³

et al. 2015

IJUNESST

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Dirty water is the world's biggest health risk. When water from rain roads into rivers, it picks up toxic chemicals, dirt, trash and disease-carrying organisms along the way. Many of our water resources lack basic protections, making them vulnerable to pollution from factory farms and industrial plants. Due to that, a classification model is needed to present the quality of the water environment. In this paper, the data mining techniques are used in this research by applying the classification method for water quality application. Various classifiers were studied in order to find the most accurate classifier for the dataset. This paper presents the comparison of accuracies for the five classifiers (NB, MLP, J48, SMO, and IBk) based on a 10-fold cross validation as a test method with respect to water quality from the datasets of Kinta River, Perak Malaysia. This study also explores which classifier is suitable to classify the dataset. The selected attributes used in this study were:

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A Natural User Interface to Facilitate Information Sharing Among Mobile Devices in a Co-located Environment

Son

Wesson

Vogts

2016

Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists

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Classification of Multivariate Data Sets without Missing Values Using Memory Based Classifiers - An Effectiveness Evaluation

Abstract: ABSTRACT

Cited by 4 publications

References 7 publications

Designing a Natural User Interface to Support Information Sharing among Co-Located Mobile Devices

Designing a Natural User Interface to Support Information Sharing among Co-Located Mobile Devices

The Study on the Accuracy of Classifiers for Water Quality Application

A Natural User Interface to Facilitate Information Sharing Among Mobile Devices in a Co-located Environment

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