{"id":215,"date":"2021-10-14T06:42:06","date_gmt":"2021-10-14T03:42:06","guid":{"rendered":"https:\/\/panagiotis-filippakis.pro\/?page_id=215"},"modified":"2026-05-22T21:31:21","modified_gmt":"2026-05-22T18:31:21","slug":"publications","status":"publish","type":"page","link":"https:\/\/panagiotis-filippakis.pro\/index.php\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"
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Multi-label prototype selection based on editing nearest neighbor rule<\/h3>\n
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Pattern Recognition,Volume 180, Part A,2026,114001,ISSN 0031-3203, 22 May 2026<\/h5>\n

Abstract<\/h2>\n

A common method to improve the efficiency of instance-based classifiers, while maintaining high accuracy, is to decrease the training set size by substituting it with a smaller, representative subset. This is typically achieved by utilizing Data Reduction Techniques (DRTs). The latter enjoy wide applicability in handling single-label classification. This is not the case though in cases of multi-label data, where each instance may be associated with multiple classes. In the present paper, we adapt a popular single-label data reduction technique, the Edited Nearest Neighbor (eNN) rule, to handle multi-label data. In single-label classification, eNN focuses on the removal of noise and close border instances making the dataset clear and the borders well-separated. The core idea is that instances with a different class than the majority of their neighbors are considered noise and are removed. In the context of multi-label data, label boundaries tend to be ambiguous, and the notion of noise is not clearly defined. Nevertheless, we hypothesize that instances whose labelsets significantly differ from those dominating their local neighborhood can be treated as noise and their removal serves to condense the training set. Based on this principle, we propose three new eNN variations for multi-label data and test them in practice. Experimental tests and statistical analysis conducted across nine (9) diverse multi-label datasets indicate that the proposed algorithms reduce significantly the size of the datasets, without compromising on accuracy, while also demonstrating superior performance compared to existing methods.<\/p>\n

Keywords: Multi-label classification, Data reduction techniques, Prototype selection, eNN rule, MLeNN, BRkNN<\/p>\n

Link: https:\/\/doi.org\/10.1016\/j.patcog.2026.114001<\/a><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Condensing multi-label data based on Clustering<\/h3>\n
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In 28th Pan-Hellenic Conference on Progress in Computing and Informatics (PCI 2024), December 13\u201315, 2024, Egaleo, Greece. ACM, New York, NY, USA, 6 pages. <\/strong><\/h5>\n

Abstract<\/strong><\/h2>\n

A common approach to speed up the instance-based classifiers,while preserving accuracy, is to use a subset of the original training set. This process, known as condensing, reduces the training dataset
\nto a smaller, representative set. This is accomplished by employing a data reduction technique that selects the most relevant training instances. While many of these techniques have been successfully
\napplied to single-label classification, most are not appropriate for multi-label data, where instances can be associated with multiple classes. This paper proposes a simple data reduction technique for
\nmulti-label datasets utilizing \ud835\udc3e-means++ clustering. The proposed method selects instances near cluster centroids to form the condensing set, utilizing \ud835\udc3e-means++\u2019s initialization strategy to achieve
\nwidely spread initial centroids. Experiments conducted on nine datasets, combined with a statistical test, show that our approach achieves significant data reduction while maintaining high classifi-
\ncation accuracy.<\/p>\n

Keywords: Multi-label classification, Data Reduction, Prototype Selection, Condensing, \ud835\udc3e-means++, BR\ud835\udc58NN<\/p>\n

Link:https:\/\/doi.org\/10.1145\/3716554.3716591<\/a><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Prototype Selection for Multilabel Instance-Based Learning<\/h3>\n
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Information<\/em>\u00a02023<\/b>,\u00a014<\/em>(10), 572; This paper is an extended version of our paper published in 27th International Database Engineered Application Symposium, IDEAS 2023, Heraklion, Greece, 5\u20137 May 2023.<\/h5>\n

Abstract<\/h2>\n
Reducing the size of the training set, which involves replacing it with a condensed set, is a widely adopted practice to enhance the efficiency of instance-based classifiers while trying to maintain high classification accuracy. This objective can be achieved through the use of data reduction techniques, also known as prototype selection or generation algorithms. Although there are numerous algorithms available in the literature that effectively address single-label classification problems, most of them are not applicable to multilabel data, where an instance can belong to multiple classes. Well-known transformation methods cannot be combined with a data reduction technique due to different reasons. The Condensed Nearest Neighbor rule is a popular parameter-free single-label prototype selection algorithm. The IB2 algorithm is the one-pass variation of the Condensed Nearest Neighbor rule. This paper proposes variations of these algorithms for multilabel data. Through an experimental study conducted on nine distinct datasets as well as statistical tests, we demonstrate that the eight proposed approaches (four for each algorithm) offer significant reduction rates without compromising the classification accuracy.<\/div>\n<\/section>\n
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Keywords:data reduction techniques<\/a>;\u00a0instance reduction<\/a>;\u00a0multilabel classification<\/a>;\u00a0prototype selection<\/a>;\u00a0instance-based classification<\/a>;\u00a0binary relevance<\/a>;\u00a0CNN<\/a>;\u00a0IB2<\/a>;\u00a0BRkNN<\/a><\/div>\n<\/div>\n
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Link:https:\/\/doi.org\/10.3390\/info14100572<\/a><\/div>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div>
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Condensed Nearest Neighbour Rules for Multi-Label Datasets<\/h3>\n
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IDEAS ’23: Proceedings of the International Database Engineered Applications Symposium Conference <\/span><\/a>May 2023<\/span><\/span>Pages 43\u201350<\/span><\/span><\/p>\n

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Abstract<\/h2>\n<\/div>\n
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Reducing the size of the training set, that is, replacing it with a condensing set, while maintaining the classification accuracy as much as possible is a very common practice to speed up instance-based classifiers. Data reduction techniques, also known as prototype selection or generation algorithms, can be used to accomplish this. There are numerous such algorithms that can be found in the literature that are effective for single-label classification problems, but the majority of them cannot be used for multi-label data where an instance may belong to multiple classes. Due to the numerous binary condensing sets it creates, the well-known Binary Relevance transformation method cannot be combined with a Data Reduction algorithm. Condensed Nearest Neighbor is a well-known parameter-free single-label prototype selection algorithm. This study proposes three variations of that algorithm for training datasets with multiple labels. An experimental study that we conducted over nine distinct datasets shows that our three proposed approaches provide good reduction rates while not tampering with the classification rates.<\/p>\n

Link: https:\/\/doi.org\/10.1145\/3589462.3589492<\/a><\/p>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Data reduction via multi-label prototype generation<\/h3>\n
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Neurocomputing,\u00a0<\/span><\/a>Volume 526<\/span><\/a>,\u00a014 March 2023, Pages 1-8<\/p>\n

Abstract<\/h2>\n
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A very common practice to speed up instance based classifiers is to reduce the size of their training set, that is, replace it by a condensing set, hoping that their accuracy will not worsen. This can be achieved by applying a Prototype Selection or Generation algorithm, also referred to as a\u00a0Data Reduction<\/a>\u00a0Technique. Most of these techniques cannot be applied on multi-label problems, where an instance may belong to more than one classes. Reduction through Homogeneous Clustering (RHC) and Reduction by Space Partitioning (RSP3) are parameter-free single-label Prototype Generation algorithms. Both are based on recursive data partitioning procedures that identify\u00a0homogeneous clusters<\/a>\u00a0of training data, which they replace by their representatives. This paper proposes variations of these algorithms for multi-label training datasets. The proposed methods generate multi-label prototypes and inherit all the\u00a0desirable properties<\/a>\u00a0of their single-label versions. They consider clusters that contain instances that share at least one common label as\u00a0homogeneous clusters<\/a>. It is shown via an experimental study based on nine multi-label datasets that the proposed algorithms achieve good reduction rates without negatively affecting\u00a0classification accuracy<\/a>.<\/p>\n

Link: https:\/\/doi.org\/10.1016\/j.neucom.2023.01.004<\/a>
\nLink in ResearchGate: Panagiotis Filippakis on ResearchGate<\/a>\n<\/p>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Prototype Generation for Multi-label Nearest Neighbours Classification<\/h3>\n
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International Conference on Hybrid Artificial Intelligence Systems\u00a0<\/a>HAIS 2021:\u00a0Hybrid Artificial Intelligent Systems<\/a>\u00a0pp 172\u2013183<\/p>\n

Abstract<\/h2>\n
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Numerous Prototype Selection and Generation algorithms for instance based classifiers and single label classification problems have been proposed in the past and are available in the literature. They build a small set of prototypes that represents as best as possible the initial training data. This set is called the condensing set and has the benefit of low computational cost while preserving accuracy. However, the proposed Prototype Selection and Generation algorithms are not applicable to multi-label problems where an instance may belong to more than one classes. The popular Binary Relevance transformation method is also inadequate to be combined with a Prototype Selection or Generation algorithm because of the multiple binary condensing sets it builds. Reduction through Homogeneous Clustering (RHC) is a simple, fast, parameter-free single label Prototype Generation algorithm that is based on\u00a0k<\/i>-means clustering. This paper proposes a RHC variation for multi-label training datasets. The proposed method, called Multi-label RHC (MRHC), inherits all the aforementioned desirable properties of RHC and generates multi-label prototypes. The experimental study based on nine multi-label datasets shows that MRHC achieves high reduction rates without negatively affecting accuracy.<\/p>\n

Link: https:\/\/doi.org\/10.1007\/978-3-030-86271-8_15<\/a><\/p>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div>

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\u0395\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc\u03c2 \u03c4\u03b1\u03be\u03b9\u03bd\u03bf\u03bc\u03b7\u03c4\u03ce\u03bd \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2 \u03b1\u03bb\u03b3\u03bf\u03c1\u03af\u03b8\u03bc\u03c9\u03bd \u03a4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u039d\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 \u03b3\u03b9\u03b1 \u03b4\u03b9\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b1\u03c3\u03b8\u03b5\u03bd\u03b5\u03b9\u03ce\u03bd<\/h3>\n
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Bachelor\u2019s thesis<\/strong><\/p>\n

Nowadays the medical profession is faced with a number of challenges since numerous diseases can be treated effectively if diagnosed correctly at an early stage. In this battle, the science of computing and some of its techniques come to our aid. To this end, there is a continuing effort to create mathematical models (classifiers) to act supportively in the correct diagnosis of illnesses such as cancer, diabetes, Alzheimer’s, heart disease, etc. The classifiers act either separately or combined to ensure maximum effectiveness. The calculating power of computing has increased significantly so it can process a bulk of data quickly in order to diagnose multifactorial diseases. The objective of this work is to study and implement the combination of these classifiers in a particular method through its application in ten datasets of different illnesses. The aim of this dissertation is to show that when the method of combining heterogenic classifiers is implemented in medical data, it can yield better illness predictions than the use of single classifiers would. This is why it has been implemented in ten different medical problems, each with its own distinctive features, so as to have as a broader view of the results it may yield as possible. The source we gathered the data of the diseases from has been of great importance in drawing conclusions. This is why we received data only from hospital doctors as well as databases available on the net. We made use of the WEKA tool and Excel to implement the classifiers used in our study, on which we developed our own computer program.<\/p>\n

Link: https:\/\/apothesis.eap.gr\/archive\/item\/143199?lang=en<\/a><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Pattern Recognition,Volume 180, Part A,2026,114001,ISSN 0031-3203, 22 May 2026 Abstract A common method to improve the efficiency of instance-based classifiers, while maintaining high accuracy, is to decrease the training set size by substituting it with a smaller, representative subset. This is typically achieved by utilizing Data Reduction Techniques (DRTs). The latter enjoy wide applicability in… \u03a0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 »Publications<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"class_list":["post-215","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/pages\/215","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/comments?post=215"}],"version-history":[{"count":56,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/pages\/215\/revisions"}],"predecessor-version":[{"id":602,"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/pages\/215\/revisions\/602"}],"wp:attachment":[{"href":"https:\/\/panagiotis-filippakis.pro\/index.php\/wp-json\/wp\/v2\/media?parent=215"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}