DECISION TREE CONSTRUCTION FOR THE CASE OF LOW-INFORMATIVE FEATURES
Keywords:decision tree, pattern recognition, classification, feature, informatineness.
Context. The problem of automating the decision tree construction is addressed. The object of study is a decision tree. The subject of study is the methods of decision tree building.
Objective. The purpose of the work is to create a method for constructing models based on decision trees for data samples that are characterized
by sets of individually low-informative features.
Method. A method for decision tree constructing is proposed, which for a given sample determines the individual informativeness of features relatively to the output feature, and also evaluates the relationship of input features with each other as their individual informativity pairwise relatively to each other, at the step of forming the next node the method selects as a candidate feature the feature that gives the best partition in the whole set of features, after which it sequentially searches among all the features that are not selected for this node the one that
is individually most closely related with the selected candidate, then for the set of selected features, iterating through the available transformations from a given set, determines the quality of the partition for each transformation, selects the best transformation and adds it to the node. When forming the next node, the method tends to single out a group of the most closely interrelated features, the conversion of which into a scalar value will provide the best partitioning of a subsample of instances hit into this node. This makes possible to reduce the size of the model and the branching of the tree, speed up the calculations in recognizing instances based on the model, as well as improve the generalizing properties of the model and its interpretability. The proposed method allows using the constructed decision tree to assess the feature significance.Results. The developed method is implemented as software and investigated at signal represented by a set of individually lowinformative readings classification problem solving. Conclusions. The experiments have confirmed the efficiency of the proposed software and allow recommending it for use in practice in solving problems of diagnostics and automatic classification by features. The prospects for further research may consist in the creation of parallel methods for constructing decision trees based on the proposed method, optimization of its software implementations, and also in an experimental study of the proposed method on a wider set of practical problems
Amit Y., Geman D., Wilder K. Joint induction of shape features
and tree classifiers, IEEE Transactions on Pattern Analysis
and Machine Intelligence, 1997, V. 19, № 11, pp. 1300–
Breiman L. L., Friedman J. H., Olshen R. A., Stone C. J.
Classification and regression trees. Boca Raton, Chapman and
Hall/CRC, 1984, 368 p.
Dietterich T. G., Kong E. B. Machine learning bias, statistical
bias, and statistical variance of decision tree algorithms [Electronic
resource]. Corvallis, Oregon State University, 1995, 14
p. Access mode:
Geurts P., Irrthum A., Wehenkel L. Supervised learning with
decision tree-based methods in computational and systems biology,
Molecular Biosystems, 2009, V. 5, No. 12, pp. 1593–
Heath D., Kasif S., Salzberg S. Induction of oblique decision
trees [Electronic resource]. Baltimor, Johns Hopkins University,
, 6 p. Access mode:
Hothorn T., Hornik K., Zeileis A. Unbiased recursive partitioning:
A conditional inference framework, Journal of Computational
and Graphical Statistics, 2006, V. 15, No. 3, pp. 651–
Hyafil L., Rivest R. L. Constructing optimal binary decision
trees is np-complete, Information Processing Letters, 1976, V.
, № 1, pp. 15–17.
Kim H., Loh W.-Y. Classification trees with unbiased multiway
splits, Journal of the American Statistical Association,
, V. 96, No. 454, pp. 589–604.
Kufrin R. Decision trees on parallel processors, Machine Intelligence
and Pattern Recognition, 1997, V. 20, pp. 279–306.
Kwok S. W., Carter C. Multiple decision trees, Fourth Annual
Conference on Uncertainty in Artificial Intelligence (UAI '88),
–12 July 1988, Minneapolis : proceedings. Amsterdam
North-Holland Publishing Co., 1990, pp. 327–338.
Mingers J. An empirical comparison of pruning methods for
decision tree induction, Machine learning, 1989, V. 4, No. 2,
Quinlan J. R. Induction of decision trees, Machine learning,
, V. 1, No. 1, pp. 81– 106.
Strobl C., Boulesteix A., Augustin T. Unbiased split selection
for classification trees based on the Gini index, Computational
Statistics & Data Analysis, 2007, V. 52, No. 1, pp. 483–501.
Utgoff P. E. Incremental induction of decision trees, Machine
learning, 1989, V. 4, No. 2, pp. 161–186. DOI:
Breiman L. Bagging predictors, Machine Learning, 1996, V.
, No. 2, pp. 123–140.
Chawla N. V. Hall L. O., Bowyer K. W., W. P. Kegelmeyer
Learning ensembles from bites: A scalable and accurate approach,
Journal of Machine Learning Results, 2004, V. 5, pp.
Dietterich T. G. An experimental comparison of three methods
for constructing ensembles of decision trees: bagging, boosting,
and randomization, Machine learning, 2000, V. 40, № 2,
Efron B. Bootstrap methods: another look at the jackknife, The
Annals of Statistics, 1979, V. 7, № 1, pp. 1–26.
Altmann A., Toloşi L., Sander O., Lengauer T. Permutation
importance: a corrected feature importance measure, Bioinformatics,
, V. 26, № 10, pp. 1340–1347.
De Mántaras R. L. A distance-based attribute selection measure
for decision tree induction, Machine learning, 1991, V. 6,
№ 1, pp. 81–92.
Deng H., Runger G., Tuv E. Bias of importance measures for
multi-valued attributes and solutions, 21st International Conference
on Artificial Neural Networks (ICANN), Espoo, 14–17
June 2011 : proceedings. Berlin, Springer-Verlag, 2011, V. 2,
Mingers J. An empirical comparison of selection measures for
decision-tree induction, Machine learning, 1989, V. 3, No. 4,
Mitchell T. Machine learning. New York, McGraw-Hill, 1997,
Painsky A., Rosset S. Cross-validated variable selection in
tree-based methods improves predictive performance, IEEE
Transactions on Pattern Analysis and Machine Intelligence,
, V. 39, No. 11, pp. 2142–2153.
Miyakawa M. Criteria for selecting a variable in the construction
of efficient decision trees, IEEE Transactions on Computers,
, V. 38, No. 1, pp. 130–141.
Tolosi L., Lengauer T. Classification with correlated features:
unreliability of feature ranking and solutions, Bioinformatics,
, V. 27, No. 14, pp. 1986–1994.
Alpaydin E. Introduction to Machine Learning. London, The
MIT Press. 2010, 400 p.
Subbotin S., Oleynik An. Entropy based evolutionary search
for feature selection, The experience of designing and application
of CAD systems in microelectronics : IX International
conference CADSM–2007, Lviv–Polyana, 20–24 February
: рroceedings. Lviv, NU “Lviv Polytechnic”, 2007, pp.
Subbotin S., Oliinyk A. Eds.: R. Szewczyk, M. Kaliczyńska
The dimensionality reduction methods based on computational
intelligence in problems of object classification and diagnosis,
Recent Advances in Systems, Control and Information Technology.
Cham, Springer, 2017, pp. 11–19. (Advances in Intelligent
Systems and Computing, vol. 543).
Subbotin S. A. Methods and characteristics of localitypreserving
transformations in the problems of computational
intelligence, Radio Electronics, Computer Science, Control,
, No. 1, pp. 120–128.
Boguslayev A. V., Oleynik Al. A., Oleynik An. A., Pavlenko
D. V., Subbotin S. A.; pod red. Pavlenko D. V., Subbotina S.
A. Progressivnyye tekhnologii modelirovaniya, optimizatsii i
intellektual'noy avtomatizatsii etapov zhiznennogo tsikla aviatsionnykh
dvigateley : monografiya. Zaporozh'ye, OAO “Motor
Sich”, 2009, 468 p.
Dubrovin V., Subbotin S., Morshchavka S., Piza D. The plant
recognition on remote sensing results by the feed-forward neural
networks, International Journal of Smart Engineering System
Design, 2001, V. 3, No. 4, pp. 251–256.
How to Cite
Copyright (c) 2019 S. A. Subbotin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.