A classification algorithm for finding the optimal rank aggregation method

Adalı, Sibel; Magdon‐Ismail, Malik; Marshall, Brandeis

doi:10.1109/iscis.2007.4456861

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Many approaches have been proposed for solving the rank aggregation problem, most of which are based on some notion of optimality that the aggregation algorithm must use to create a fully ordered list from the different total or partial lists provided by individual sources. 43 However, there are rank aggregation methods that are not optimization-based but are still effective in practice. In particular, Markov chain-based methods have proven to be very effective.…”

Section: Feature Selectionmentioning

confidence: 99%

Novel machine learning insights into the QM7b and QM9 quantum mechanics datasets

Valdés,

Tchagang

2024

J Comput Chem

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This paper (i) explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic properties, and (ii) further explores an inverse design approach to molecular design consisting of using machine learning methods to approximate the atomic composition of molecules, using QM9 data. Understanding the structure and characteristics of this kind of data is important when predicting the atomic composition from physical‐chemical properties in inverse molecular designs. Intrinsic dimension analysis, clustering, and outlier detection methods were used in the study. They revealed that for both datasets the intrinsic dimensionality is several times smaller than the descriptive dimensions. The QM7b data is composed of well‐defined clusters related to atomic composition. The QM9 data consists of an outer region predominantly composed of outliers, and an inner, core region that concentrates clustered inliner objects. A significant relationship exists between the number of atoms in the molecule and its outlier/inliner nature. The spatial structure exhibits a relationship with molecular weight. Despite the structural differences between the two datasets, the predictability of variables of interest for inverse molecular design is high. This is exemplified by models estimating the number of atoms of the molecule from both the original properties and from lower dimensional embedding spaces. In the generative approach the input is given by a set of desired properties of the molecule and the output is an approximation of the atomic composition in terms of its constituent chemical elements. This could serve as the starting region for further search in the huge space determined by the set of possible chemical compounds. The quantum mechanic's dataset QM9 is used in the study, composed of 133,885 small organic molecules and 19 electronic properties. Different multi‐target regression approaches were considered for predicting the atomic composition from the properties, including feature engineering techniques in an auto‐machine learning framework. High‐quality models were found that predict the atomic composition of the molecules from their electronic properties, as well as from a subset of only 52.6% size. Feature selection worked better than feature generation. The results validate the generative approach to inverse molecular design.

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Section: Feature Selectionmentioning

confidence: 99%

Novel machine learning insights into the QM7b and QM9 quantum mechanics datasets

Valdés,

Tchagang

2024

J Comput Chem

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“…The relevance scores can then be used to sort the documents to produce the final ranked list of results. Several different pointwise methods have been proposed in the literature, including the Additive Groves algorithm by Sorokina et al (2007), RankClass (Ji et al, 2011), the algorithm proposed by Adali et al (2007) and random model trees (Pfahringer, 2011). • Pairwise approach -L2R4IR is seen as a binary classification problem for document pairs because the relevance degree can be regarded as a binary value that tells which document order is better for a given pair of documents. Given the feature vectors of pairs of documents from the data for the input space, the relevance degree of each of those documents can be predicted with scoring functions that attempt to minimise the average number of misclassified pairs.…”

Section: Literature Regarding Learning To Rank Algorithmsmentioning

confidence: 99%

Section: Literature Regarding Learning To Rank Algorithmsmentioning

confidence: 99%

Learning to rank academic experts in the DBLP dataset

2013

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Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework.Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.

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A classification algorithm for finding the optimal rank aggregation method

Cited by 2 publications

References 12 publications

Novel machine learning insights into the QM7b and QM9 quantum mechanics datasets

Novel machine learning insights into the QM7b and QM9 quantum mechanics datasets

Learning to rank academic experts in the DBLP dataset

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