Pith. sign in

REVIEW 3 major objections 3 minor 34 references

Clustering Wi-Fi Fingerprints for Indoor-Outdoor Detection

T0 review · 3 major / 3 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that continuous indoor-outdoor detection can be achieved with nothing but Wi-Fi fingerprints, without site surveys, radio maps, or data from the target device.

desk verdict Useful dataset and a graph idea worth engaging with, but the headline AUC is an offline, temporally leaked number and the continuous-detection claim is not supported by the experiments. read the letter →

arxiv 1908.00758 v1 pith:X2ULEYLT submitted 2019-08-02 eess.SP cs.HC

Guy Shtar , Bracha Shapira , Lior Rokach This is my paper · ORCID
classification eess.SPcs.HC
keywords indoor-outdoordetectionWi-FifingerprintingclustertransitiongraphrankcorrelationdistanceDBSCANgradientboostingcontextawarenessmobilesensing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that continuous indoor-outdoor detection can be achieved with nothing but Wi-Fi fingerprints, and that the classifier does not need a site survey, a radio map, or data from the target device. The proposed pipeline clusters fingerprints with a rank-correlation distance, links clusters into a transition graph, and trains a supervised model on neighborhood features of the graph nodes; a gradient-boosting model reaches an area under the curve of 0.94 and accuracy of 0.93 in a study of 12 participants over 828 hours. The authors argue this works in unknown locations and on seven unseen phone models, because the graph abstracts away device-specific signal characteristics. The practical payoff, if true, is a context signal that mobile apps can obtain from a sensor that is already likely to be on for indoor positioning.

What carries the argument

The central object is the cluster transition graph. Wi-Fi fingerprints from a device are clustered with DBSCAN using a Spearman-rank-correlation distance adapted to sparse scans (access points seen by only one scan are ranked last; access points unseen by both are ignored); clusters become nodes, and an edge joins two nodes whenever they contain temporally consecutive scans. Each node is then described by four features computed over graph neighborhoods of several sizes: number of neighbor nodes, average received signal strength, average number of access points per scan, and average number of fingerprints. The graph is the load-bearing abstraction: the paper shows that classifying these nodes, weighted by cluster size, outperforms classifying raw fingerprints or clusters directly.

What would settle it

Run the exact trained model in live streaming mode on a new device for a full day, building the transition graph incrementally from past scans only, and track per-minute AUC across all environment changes including underground garages and no-Wi-Fi areas; the claim fails if the live AUC drops decisively below 0.94 or if the graph-based model no longer beats the raw-fingerprint model.

Watch

Extended reading notes

Core claim

The central discovery is that a transition graph built from clustered Wi-Fi fingerprints carries a stable, device-independent signature of indoor versus outdoor state. In the authors' account, outdoor fingerprints string out into long chains in the graph, while indoor fingerprints cluster into dense areas; neighborhood statistics computed from the graph therefore separate the two states far better than raw fingerprints or cluster labels alone. Training the model on roughly 22 hours of data collected with one device in a few locations, then testing on 12 participants using seven device models in their daily routines, yields a mean AUC of 0.94 and accuracy of 0.93, with the graph-based features statistically significantly outperforming cluster- and fingerprint-level features. A smaller online feasibility test shows a warm-up time of roughly one minute in most new environments, with the acknowledged exception of an underground parking garage where the Wi-Fi environment looks outdoor-like.

Load-bearing premise

The load-bearing premise is that the transition graph built from a complete recorded trace matches what a real-time continuous system would have available, since the headline AUC is measured offline on graphs constructed from all of each user's data and the online test covers only eight short scenarios on two devices.

Editorial extensions

If this is right

  • A mobile OS could maintain a live transition graph from ordinary Wi-Fi scans and label indoor/outdoor without GPS, saving battery and working where satellite signals are weak.
  • The few-hours single-device training recipe could be reused at scale, replacing per-building radio maps with one short calibration pass per region or device class.
  • Because the graph features improve results at neighborhood distances up to 30 edges, the method can tolerate temporary gaps or noise in individual scans.
  • The method transfers to unseen devices and locations only if the graph abstraction really smooths out device-specific RSSI biases, which the seven-model test is the paper's evidence for.
  • The same graph representation makes other logical-context labels, such as building or room identity, plausible outputs, as the paper begins to show.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The rank-correlation distance is probably what buys device invariance, since ranking strips out per-device RSSI scaling; a direct ablation that replaces Spearman with Euclidean or Manhattan distance on the same datasets would make that causal story explicit.
  • The underground-parking failure suggests the classifier implicitly learns that indoor places have many strong access points; a testable fix is to add a weak-signal prior so sparse indoor environments do not collapse to the outdoor class.
  • A true streaming version needs incremental clustering and incremental graph updates; the reported one-minute warm-up was measured by repeatedly re-walking ten-minute traces, so it is not yet proof of an online system that never sees future fingerprints.
  • The same transition graph could feed an unsupervised change-point detector to recognize room transitions or entry and exit moments, extending the method to context segmentation rather than classification alone.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The paper proposes an indoor/outdoor detection method based solely on Wi-Fi fingerprints, using DBSCAN clustering of fingerprints (with a Spearman-rank distance), a cluster transition graph, and node-level features extracted from graph neighborhoods. A supervised classifier (primarily random forest and gradient boosting) is trained on data collected with a single device by the research team and evaluated on 12 participants using their personal phones in daily life, for a total of 828 hours. The authors report an AUC of 0.94 and accuracy of 0.93 with gradient boosting, and they claim that a few hours of training data from one device suffice for continuous indoor/outdoor detection in unknown locations and on new devices.

Significance. If the central claim were supported, the method would be a practically valuable contribution: it uses only Wi-Fi, requires no site survey, and is intended to work across devices and locations. The paper also makes several commendable choices: evaluation on held-out users, multiple device models, a large real-world dataset, public release of dataset and code, and the use of AUC rather than raw accuracy for an imbalanced task. However, the headline result is undermined by a temporal leakage in the offline evaluation, and the online feasibility test is too limited and itself not fully causal. The strengths are real, but the evidence as presented does not establish the paper's stated continuous-detection claim.

major comments (3)
  1. [§4.1, §5.1, Table 3] The headline AUC of 0.94 is computed with features that include future information. Section 4.1 states that 'a transition graph was constructed for each of the devices' data separately', and Section 3.4 defines node features via a breadth-first search over that graph, i.e., the neighborhood N_x(d) of a node. For a fingerprint collected at time t, the graph built from the complete device trace contains clusters and fingerprints collected after t, so the 'number of neighbors', 'average power', 'average AP count', and 'average fingerprint count' features for that fingerprint depend on future scans. The DBSCAN cluster assignments themselves also depend on the full trace. Consequently, Table 3 measures transductive accuracy on a complete trace, not the accuracy of a classifier that makes predictions at time t. The abstract's statement that 'a training set ... is sufficient in order to provide indoor-outdoor classification' and the specific AUC claim for 'continuous' detection are not supported by this evaluation.
  2. [§5.2, Figures 6–13] The online feasibility test does not remove the temporal leakage. The authors state that 'we constructed a transition graph using the data collected during the first minute and classified the fingerprints. We repeated the process by adding data obtained during each additional minute gradually and evaluated the data collected from the beginning of the test until that minute.' This means that fingerprints from minute 1 are evaluated using a graph that includes data from minutes 2 through k, so even this experiment is not a per-fingerprint causal evaluation. In addition, the test covers only eight ten-minute single-class scenarios, uses only two devices, reports accuracy rather than AUC, and the underground parking scenario (scenario 6) is explicitly omitted after the classifier failed. As a result, Section 5.2 provides only a weak, partial indication of online performance and does not establish the continuous-detection claim.
  3. [Abstract, §6, Table 3] The reported AUC is inconsistent with the presented results. The abstract and Section 6 report an AUC of 0.94, but the average GBM graph AUC in Table 3 is 0.92 (and the average graph accuracy is 0.92, not 0.93 as stated in the abstract). The authors should explain how the headline 0.94 is obtained; otherwise the discrepancy undermines confidence in the reported performance.
minor comments (3)
  1. [§3.2.2.1, Eq. (8)] Equation (8) is difficult to read: the cases are not clearly separated and the expression '2 2' appears twice in the first two lines. Please reformat the piecewise definition for clarity.
  2. [§5.2, Table 4] The device names in the text ('Nexus 3 (GT-I9250)') appear inconsistent with common model names; please verify the model designations.
  3. [Figures 14–15] The captions of Figures 14 and 15 could state whether the plots aggregate over both devices and all scenarios; currently the reader must infer this from the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the classifier is trained on user 0 and evaluated on held-out users; graph leakage is a validity concern, not circularity.

full rationale

The claimed derivation is not circular. The supervised classifier is trained exclusively on User 0's data (Section 4.1: 'The training set was collected by the research team...'), and the held-out users 1-12 provide the test set; no test labels or test-device parameters are used to fit the model or to select node features, which are graph-based statistics (number of neighbors, average power, AP count, fingerprint count) computed from unsupervised DBSCAN clusters. The distance measure and transition graph are defined independently of the indoor/outdoor label. The only self-citation ([15], a book by co-author Lior Rokach) supports the choice of AUC and is corroborated by external references [14,16]; it does not carry the derivation. Section 5.2 candidly acknowledges that the main experiment is offline ('The main drawback of the experiment described above is its offline processing...') and that online conversion is future work; constructing each test device's transition graph from its full trace introduces temporal leakage, but this is an evaluation-timing flaw, not a reduction of the prediction to its training inputs. Accordingly, no circular step can be exhibited, and the score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method relies on tuned clustering parameters and domain assumptions about fingerprint stability and label quality; it introduces no new physical entities.

free parameters (3)
  • eps (DBSCAN neighborhood size) = 0.22
    Selected via location-based cross-validation on the training set; controls cluster granularity and all downstream graph features.
  • MinPts (DBSCAN minimum cluster size) = 1
    Set to 1 so no fingerprint is treated as noise; affects cluster structure.
  • Neighborhood distances d for graph features = ranges in Table 2 (number of neighbors d=2..6, other features d=0..4)
    Selected via linear regression significance tests on the training set; each feature uses a different d range.
assumptions (4)
  • domain assumption Wi-Fi fingerprints are stable enough within a location that DBSCAN clusters correspond to meaningful spatial regions
    The entire clustering step assumes that similar RSSI vectors co-occur spatially; used in Section 3.2.
  • domain assumption The rank-based distance measure (adapted Spearman) preserves indoor/outdoor discriminative structure
    Section 3.2.1 defines the distance; no independent validation that this distance optimizes indoor-outdoor separation.
  • domain assumption Self-reported indoor/outdoor labels from participants are accurate
    Ground truth is collected via a checkbox in the app, Section 4.1; label noise is not modeled.
  • ad hoc to paper A graph built from the full device trace represents the graph an online system would have after warm-up
    Section 5.1 builds one transition graph per device from all data, including future fingerprints; Section 5.2 only partially tests the online setting.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Clustering Wi-Fi Fingerprints for Indoor-Outdoor Detection." pith.science (2026). https://pith.science/paper/X2ULEYLT

@misc{pith2026190800758,
  author       = {Pith},
  title        = {Pith review of: Clustering Wi-Fi Fingerprints for Indoor-Outdoor Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X2ULEYLT}},
  note         = {Machine review of arXiv:1908.00758}
}
read the original abstract

This paper presents a method for continuous indoor-outdoor environment detection on mobile devices based solely on WiFi fingerprints. Detection of indoor outdoor switching is an important part of identifying a user's context, and it provides important information for upper layer context aware mobile applications such as recommender systems, navigation tools, etc. Moreover, future indoor positioning systems are likely to use Wi-Fi fingerprints, and therefore Wi-Fi receivers will be on most of the time. In contrast to existing research, we believe that these fingerprints should be leveraged, and they serve as the basis of the proposed method. Using various machine learning algorithms, we train a supervised classifier based on features extracted from the raw fingerprints, clusters, and cluster transition graph. The contribution of each of the features to the method is assessed. Our method assumes no prior knowledge of the environment, and a training set consisting of the data collected for just a few hours on a single device is sufficient in order to provide indoor-outdoor classification, even in an unknown location or when using new devices. We evaluate our method in an experiment involving 12 participants during their daily routine, with a total of 828 hours' worth of data collected by the participants. We report a predictive performance of the AUC (area under the curve) of 0.94 using the gradient boosting machine ensemble learning method. We show that our method can be used for other context detection tasks such as learning and recognizing a given building or room.

Figures

Figures reproduced from arXiv: 1908.00758 by the authors.

Figure 1
Figure 1. Fingerprint to AP and AP to fingerprint mapping [PITH_FULL_IMAGE:figures/full_fig_p030_1.png] view at source ↗
Figure 3
Figure 3. The Android application for data collection used in the experiment [PITH_FULL_IMAGE:figures/full_fig_p031_3.png] view at source ↗
Figure 4
Figure 4. Comparison between various classifiers for all of the devices 0.89 0.94 0.80 0.91 0.70 0.90 0.80 0.87 0.65 0.82 0.91 0.89 0.91 0.88 0.59 0.93 0.93 0.89 0.84 0.86 0.50 0.60 0.70 0.80 0.90 1.00 Gradient Boosting Machine Random Forest Rotation Forest Bagged CART Naïve Bayes AUC - Clusters AUC - Graph Accuracy - Clusters Accuracy - Graph [PITH_FULL_IMAGE:figures/full_fig_p031_4.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 23 canonical work pages

  1. [1]

    IEEE Communications Surveys & Tutorials 16(1), 414–454

    Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials 16(1), 414–454. doi:10.1109/SURV.2013.042313.00197

    work page arXiv 2014

  2. [2]

    D, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A

    Lane N. D, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A. T (2010) A survey of mobile phone sensing. IEEE Communications Magazine 48(9), 140–150. doi:10.1109/MCOM.2010.5560598

    arXiv 2010

  3. [3]

    Sensors 16(2), 268

    Zou H, Jiang H, Luo Y, Zhu J, Lu X, Xie L (2016) Bluedetect: An ibeacon-enabled scheme for accurate and energy-efficient indoor-outdoor detection and seamless location-based service. Sensors 16(2), 268. doi:10.3390/s16020268

    work page doi:10.3390/s16020268 2016

  4. [4]

    doi:10.3390/s16101563

    Wang W, Chang Q, Li Q, Shi Z, Chen W (2016) Indoor-Outdoor Detection Using a Smart Phone Sensor Sensors 16(10), 1563. doi:10.3390/s16101563

    work page doi:10.3390/s16101563 2016

  5. [5]

    Proceedings of the 8th USENIX conference on Networked systems design and implementation (NSDI’11), 281–294

    Ravindranath L, Newport C, Balakrishnan H, Madden S (2011) Improving wireless network performance using sensor hints. Proceedings of the 8th USENIX conference on Networked systems design and implementation (NSDI’11), 281–294

    work page 2011

  6. [6]

    The 11th International Symposium on Location Based Services

    Cho H, Song J, Park H, Hwang C (2014) Deterministic Indoor Detection from Dispersions of GPS Satellites on the Celestial Sphere. The 11th International Symposium on Location Based Services

    work page 2014

  7. [7]

    Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, 113–

    Zhou P, Zheng Y, Li Z, Li M, Shen G (2012) IODetector: A generic service for Indoor Outdo or Detection. Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, 113–

    work page 2012

  8. [8]

    C, Goncalves J, Ferreira D, Hosio S, Kostakos V (2017) Environmental exposure assessment using indoor/outdoor detection on smartphones

    Anagnostopoulos T, Garcia J. C, Goncalves J, Ferreira D, Hosio S, Kostakos V (2017) Environmental exposure assessment using indoor/outdoor detection on smartphones. Personal and Ubiquitous Computing 21(4), 761-773. doi:10.1007/s00779-017-1028-y

    work page doi:10.1007/s00779-017-1028-y 2017

Show all 34 references
  1. [9]

    K (2014) A semi-supervised learning approach for robust indoor-outdoor detection with smartphones

    Radu V, Katsikouli P, Sarkar R, Marina M. K (2014) A semi-supervised learning approach for robust indoor-outdoor detection with smartphones. Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems (SenSys ’14), 280–294. doi:10.1145/2668332.2668347

    2014

  2. [10]

    Proceedings of the 11th International Conference on Mobile and 22 Ubiquitous Systems: Computing, Networking and Services (MobiQuitous '14), 352–353

    Canovas O, Lopez-de-Teruel P, Ruiz A (2014) WiFiBoost: a terminal-based method for detection of indoor/outdoor places. Proceedings of the 11th International Conference on Mobile and 22 Ubiquitous Systems: Computing, Networking and Services (MobiQuitous '14), 352–353. doi:10.41...

    2014

  3. [11]

    N, Karnal H, Hogrefe D (2015) Knowledge-assisted location-adaptive technique for indoor-outdoor detection in e-learning

    Edelev S, Prasad S. N, Karnal H, Hogrefe D (2015) Knowledge-assisted location-adaptive technique for indoor-outdoor detection in e-learning. IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). doi:10.1109/PERCOMW.2015.7133985

    2015

  4. [12]

    H (2016) Towards area classification for large-scale fingerprint-based system

    He S, Tan J, Gary Chan S. H (2016) Towards area classification for large-scale fingerprint-based system. Proceedings of the 2016 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp '16), 232–243. doi: 10.1145/2971648.2971689

    2016

  5. [13]

    Anagnostopoulos, Michel Deriaz (2015) Automatic switching between indoor and outdoor position providers

    Grigorios G. Anagnostopoulos, Michel Deriaz (2015) Automatic switching between indoor and outdoor position providers. International Conference on Indoor Positioning and Indoor Navigation (IPIN). doi: 10.1109/IPIN.2015.7346948

    2015

  6. [14]

    E (2009) Learning from imbalanced data

    He H, Garcia A. E (2009) Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9), 1263–1284. doi:10.1109/TKDE.2008.239

    2009 doi

  7. [15]

    Singapore: World Scientific, 34 and 43

    Rokach L, Maimon O (2014) Data Mining with Decision Trees. Singapore: World Scientific, 34 and 43

    2014

  8. [16]

    X (2005) Using AUC and accuracy in evaluating learning algorithms

    Huang J, Ling C. X (2005) Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17(3), 299–310. doi:10.1109/TKDE.2005.50

    2005 doi

  9. [17]

    N (2000) RADAR: an in-building RF-based user location and tracking system

    Bahl P, Padmanabhan V. N (2000) RADAR: an in-building RF-based user location and tracking system. Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2000), 775–784. doi:10.1109/INFCOM.2000.832252

    2000

  10. [18]

    N (2010) Indoor localization without the pain

    Chintalapudi K, Padmanabha Iyer A, Padmanabhan V. N (2010) Indoor localization without the pain. Proceedings of the sixteenth annual international conference on Mobile computing and networking (MobiCom ’10), 173–184. doi:10.1145/1859995.1860016

    2010

  11. [20]

    R (2012) No Need to War-Drive: Unsupervised Indoor 23 Localization

    Wang H, Elgohary A, Choudhury R. R (2012) No Need to War-Drive: Unsupervised Indoor 23 Localization. Proceedings of the 10th international conference on Mobile systems, applications, and services (MobiSys ’12), 197–210. doi:10.1145/2307636.2307655

    2012

  12. [21]

    ACM Transactions on Sensor Networks (TOSN) 13(2)

    Teng X, Guo D, Guo Y, Zhou X, Ding Z, Liu Z (2017) IONavi: An Indoor-Outdoor Navigation Service via Mobile Crowdsensing. ACM Transactions on Sensor Networks (TOSN) 13(2). doi: 10.1145/3043948

    2017 doi

  13. [22]

    Proceedings of the 9th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous '12), 212–223

    Bhargava P, Krishnamoorthy S, Karkada Nakshathri A, Mah M, Agrawala A (2012) Locus: An Indoor Localization, Tracking and Navigation System for Multi-story Buildings Using Heuristics Derived from Wi-Fi Signal Strength. Proceedings of the 9th International Conference on Mobile a...

    2012 doi

  14. [23]

    IEEE Transactions on Parallel and Distributed Systems 24(4), 839–848

    Wu C, Yang Z, Liu Y, Xi W (2013) WILL: Wireless Indoor Localization without Site Survey. IEEE Transactions on Parallel and Distributed Systems 24(4), 839–848. doi:10.1109/TPDS.2012.179

    2013 doi

  15. [24]

    IEEE Transactions on Multimedia 15(4), 858–869

    Bisio I, Lavagetto F, Marchese M, Sciarrone A (2013) GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphones in Cloud-Based LBSs. IEEE Transactions on Multimedia 15(4), 858–869. doi:10.1109/TMM.2013.2239631

    2013

  16. [25]

    IEEE 13th International Conference on Mobile Data Management (MDM), 282–287

    Dousse O, Eberle J, Mertens M (2012) Place Learning via Direct WiFi Fingerprint Clustering. IEEE 13th International Conference on Mobile Data Management (MDM), 282–287. doi:10.1109/MDM.2012.46

    2012 doi

  17. [26]

    Proceedings of the 10th international conference on Pervasive Computing (Pervasive '12), pp

    Pulkkinen T, Nurmi P (2012) AWESOM: Automatic Discrete Partitioning of Indoor Spaces for WiFi Fingerprinting. Proceedings of the 10th international conference on Pervasive Computing (Pervasive '12), pp. 271–288. doi:10.1007/978-3-642-31205-2_17

    2012 doi

  18. [27]

    D, Kim Y, Estrin D, Srivastava M

    Kim H. D, Kim Y, Estrin D, Srivastava M. B (2010) SensLoc: Sensing Everyday Places and Paths using Less Energy Proc. Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys '10), 43–56. doi:10.1145/1869983.1869989

    2010

  19. [28]

    Proceedings of the 18th annual international conference on Mobile computing and networking (Mobicom '12), 269–280

    Yang Z, Wu C, Liu Y (2012) Locating in fingerprint space: Wireless indoor localization with little human intervention. Proceedings of the 18th annual international conference on Mobile computing and networking (Mobicom '12), 269–280. doi:10.1145/2348543.2348578 24

    2012

  20. [29]

    2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN) doi: 10.1109/IPIN.2011.6071929

    Machaj J, Brida P, Piché R (2012) Rank based fingerprinting algorithm for indoor positioning. 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN) doi: 10.1109/IPIN.2011.6071929

    2012

  21. [30]

    L, Well A

    Myers J. L, Well A. D, Lorch R. F. Jr, (2013) Research Design and Statistical Analysis. Routledge. Mahwah, NJ: Lawrence Erlbaum Associates

    2013

  22. [31]

    P, Sander J, Xu X (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

    Ester M, Kriegel H. P, Sander J, Xu X (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD '96), 226–231

    1996

  23. [32]

    H (2001) Greedy Function Approximation: A Gradient Boosting Machine

    Friedman J. H (2001) Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics 29(5), 1189–1232. doi:10.1214/aos/1013203451

    2001

  24. [33]

    Machine Learning 45(1), 5–32

    Breiman L (2001) Random Forests. Machine Learning 45(1), 5–32. doi:10.1023/A:1010933404324

    2001 doi

  25. [34]

    J, Kuncheva L

    Rodríguez J. J, Kuncheva L. I, Alonso C. J (2006) Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630. doi:10.1109/TPAMI.2006.211 Tables User Device Model Hours Total FP Unlabeled FP Indoors FP Out...

    2006 doi

  26. [126]

    doi:10.1145/2426656.2426668

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.