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Table 1 A summary of machine learning classification techniques used for various context-aware mobile services and systems

From: Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

Classifiers Purposes References
ZeroR Instant messaging Fetter et al. [22]
RIDOR Activity recognition, notification management, location prediction, interruptibility prediction Ayu et al. [25], Poppinga et al. [24], Anagnostopoulos et al. [23], Turner et al. [38]
RIPPER Location prediction Anagnostopoulos et al. [23]
KNN Mobile search, recommender system, location prediction, activity recognition, interruptibility prediction Swati et al. [28], Middleton et al. [30], Bozanta et al. [29], Anagnostopoulos et al. [23], Ayu et al. [25], Fisher et al. [31], Turner et al. [38]
NB Phone call prediction, location prediction, interruption management Sarker et al. [33, 36], Pejovic et al. [3], Ayu et al. [25], Fogarty et al. [35], Fisher et al. [31]
LR Activity recognition, user modeling, recommendation system, health analytics, interruptibility prediction Riboni et al. [40], Zhong et al. [41], Wang et al. [42], Ernsting et al. [44], Turner et al. [37, 38]
SVM Instant messaging, transportation system, activity recognition, notification management, interruptibility prediction Pielot et al. [46], Bedogni et al. [34], Bayat et al. [47], Ayu et al. [25], Fetter et al. [22], Turner et al. [37, 38], Fogarty et al. [35], Fisher et al. [31] Turner et al. [38]
DT Context-aware system, mobile service, interruption management, interruptibility prediction, phone call prediction Hong et al. [51], Lee et al. [52], Zulkernain et. al. [53], Turner et al. [37, 38], Sarker et al. [33, 36], Fogarty et al. [35], Fisher et al. [31]
RF Call availability prediction, instant messaging, transportation system, activity recognition, interruptibility prediction Pielot et al. [46, 57], Bedogni et al. [34], Bayat et al. [47], Ayu et al. [25], Turner et al. [37, 38]
AdaBoost Interruption management, interruptibility prediction, location prediction, recommeder system Pejovic et al. [3], Turner et al. [37, 38], Anagnostopoulos et al. [23], Fogarty et al. [35], Middleton et al. [30]
ANN Smartphone power modeling, mobile credit card payment, mobile commerce, mobile learning, smartphone characterization Alawnah et al. [59], Leong et al. [60], Chong et al. [61], Tan et al. [62], Rajashekar et al. [63]