25 June 2015

Michael Khachay visited ASA, IIASA

Professor Michail Khachay from Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia visited the Advanced System Analysis program on 25-26 June 2015 and gave talks on Ensemble Techniques in Machine Learning and on Approximability to the Multiple Salesmen Problem. 

Ensemble techniques in machine learning

This talk can be considered as a brief introduction to collective (ensemble) learning methods, which is one of the most prominent fields of modern machine learning. Basically, an ensemble classifier (also known as a committee) is a collective (ensemble) of rather rough classifiers whose decisions are aggregated into the overall decision using the simple majority voting technique. Nevertheless, as it follows from the famous results by R.Schapire, Y.Freund, L.Breiman, V.Mazurov et al., such a simple aggregation rule has a wonderful power in increasing of performance of resulting ensembles. Generally speaking, for any given accuracy bound, to produce a committee decision rule having misclassification probability less than this bound, it is sufficient to provide a class of weak classifiers, such that, for any finite training sample, there is a classifier making it’s decision better then random guessing. This shining result accompanying by theory of V. Vapnik and A. Chervonenkis provides a possibility to construct decision rules and forecasting technique of very high performance. There are several methods to produce such an ensembles, among them are boosting by R. Schapire and Y. Freund, bagging by L.Breiman, and committee method by V. Mazurov. In the presentation, the famous Adaboost algorithm and Mazurov’s committee method closely related to it are the main objects of interest. The subjects under consideration are non-formal description of the algorithms, illustrative examples, curse of overfitting during the leaning by finite training sample, and the ability of boosting algorithms to overcome it automatically by maximizing of learning margin.

Approximability to the Multiple Salesmen Problem

Multiple Salesmen Problem appears to be a natural generalization of the well-known Traveling Salesman combinatorial optimization problem (TSP) and is closely related to multiple statements of Vehicle Routing Problem (VRP). All these problems are strongly NP-hard, i.e. they can not be solved efficiently unless P = NP . Also, all of them are hardly approximable in general case. But, for more specific settings of TSP and VRP, there are known efficient approximation algorithms with theoretically proven performance guarantees and even polynomial time approximation schemes (PTAS). A combinatorial minimization problem has PTAS if for any instance of the problem and for any given ε > 0 the optimum can be approximated within a factor 1 + ε in polynomial time of the instance length (but depending on 1/ε).

It is shown that

(i) Multiple Salesmen Problem is strongly NP-hard and hardly approximable in general case;

(ii) Metric setting of the problem can be approximated within an attainable factor 2;

(iii) Euclidean setting of the problem has PTAS for any fixed dimension.

Prof. Michael Khachay is a head of mathematical programming lab. at Krasovsky Institute of Mathematics and Mechanics Ural Branch of Russian Academy of Science, Ekaterinburg Russia. Also, he is a Prof. of the Chair of Mathematical Economy at Ural Federal University. He got his PhD (Candidate of Science degree in Russia) on mathematical cybernetics in 1996 at Krasovsky Institute of Mathematics and Mechanics, and got habilitation (Doctor of Science in Russia) on discrete mathematics and mathematical cybernetics in 2005 at Dorodnitsyn Computing Center, Moscow. The scientific scope of M. Khachay includes theory and methods of ensemble machine learning, combinatorial optimization, complexity, and efficient approximation of NP-hard combinatorial problems. In particular, he proposed sufficient conditions of existence for several kinds of ensembles, investigated computational complexity and approximability of problems related to optimal ensemble learning. Also, he proposed polynomial time approximation algorithms with state-of-the-art performance guarantees and polynomial time approximation schemes for several NP-hard problems of combinatorial optimization.

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Last edited: 26 June 2015


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