NDLI: Qualitative and quantitative scoring and evaluation of the eye movement classification algorithms

Content Provider	ACM Digital Library
Author	Koh, Do Hyong Gowda, Sandeep Munikrishne Jayarathna, Sampath Komogortsev, Oleg V.
Abstract	This paper presents a set of qualitative and quantitative scores designed to assess performance of any eye movement classification algorithm. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (I-HMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the "best" classification algorithm is provided for several applications. General recommendations for the selection of the input parameters for each algorithm are provided.
Starting Page	65
Ending Page	68
Page Count	4
File Format	PDF
ISBN	9781605589947
DOI	10.1145/1743666.1743682
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-03-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Metrics Analysis Classification Scoring Eye movements Algorithm
Content Type	Text
Resource Type	Article

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Qualitative and quantitative scoring and evaluation of the eye movement classification algorithms