The Experts below are selected from a list of 64209 Experts worldwide ranked by ideXlab platform

Paul H Leng - One of the best experts on this subject based on the ideXlab platform.

  • Setting attribute weights for k-NN based Binary Classification via quadratic programming
    Intelligent Data Analysis, 2003
    Co-Authors: Lu Zhang, Frans Coenen, Paul H Leng
    Abstract:

    The k-Nearest Neighbour (k-NN) method is a typical lazy learning paradigm for solving Classification problems. Although this method was originally proposed as a non-parameterised method, attribute weight setting has been commonly adopted to deal with irrelevant attributes. In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, which is particularly suitable for Binary Classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. To evaluate our method, we carried out a series of experiments on six established data sets. Experiments show that our method is quite practical for various problems and can achieve a stable increase in accuracy over the standard k-NN method as well as a competitive performance. Another merit of the method is that it can use small training sets.

  • an attribute weight setting method for k nn based Binary Classification using quadratic programming
    European Conference on Artificial Intelligence, 2002
    Co-Authors: Lu Zhang, Frans Coenen, Paul H Leng
    Abstract:

    In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, which is particular suitable for Binary Classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. Experiments show that our method is quite practical for various problems and can achieve a competitive performance. Another merit of the method is that it can use small training sets.

Giuseppe Jurman - One of the best experts on this subject based on the ideXlab platform.

  • the advantages of the matthews correlation coefficient mcc over f1 score and accuracy in Binary Classification evaluation
    BMC Genomics, 2020
    Co-Authors: Davide Chicco, Giuseppe Jurman
    Abstract:

    To evaluate Binary Classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in Binary Classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. In this article, we show how MCC produces a more informative and truthful score in evaluating Binary Classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating Binary Classification tasks by all scientific communities.

Lu Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Setting attribute weights for k-NN based Binary Classification via quadratic programming
    Intelligent Data Analysis, 2003
    Co-Authors: Lu Zhang, Frans Coenen, Paul H Leng
    Abstract:

    The k-Nearest Neighbour (k-NN) method is a typical lazy learning paradigm for solving Classification problems. Although this method was originally proposed as a non-parameterised method, attribute weight setting has been commonly adopted to deal with irrelevant attributes. In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, which is particularly suitable for Binary Classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. To evaluate our method, we carried out a series of experiments on six established data sets. Experiments show that our method is quite practical for various problems and can achieve a stable increase in accuracy over the standard k-NN method as well as a competitive performance. Another merit of the method is that it can use small training sets.

  • an attribute weight setting method for k nn based Binary Classification using quadratic programming
    European Conference on Artificial Intelligence, 2002
    Co-Authors: Lu Zhang, Frans Coenen, Paul H Leng
    Abstract:

    In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, which is particular suitable for Binary Classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. Experiments show that our method is quite practical for various problems and can achieve a competitive performance. Another merit of the method is that it can use small training sets.

Manfred Claassen - One of the best experts on this subject based on the ideXlab platform.

  • a fused elastic net logistic regression model for multi task Binary Classification
    arXiv: Machine Learning, 2013
    Co-Authors: Venelin Mitov, Manfred Claassen
    Abstract:

    Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations. We present the fused logistic regression, a sparse multi-task learning approach for Binary Classification. Specifically, we introduce sparsity inducing penalties over parameter differences of related logistic regression models to encode similarity across related tasks. The resulting joint learning task is cast into a form that lends itself to be efficiently optimized with a recursive variant of the alternating direction method of multipliers. We show results on synthetic data and describe the regime of settings where our multi-task approach achieves significant improvements over the single task learning approach and discuss the implications on applying the fused logistic regression in different real world settings.

Frans Coenen - One of the best experts on this subject based on the ideXlab platform.

  • Setting attribute weights for k-NN based Binary Classification via quadratic programming
    Intelligent Data Analysis, 2003
    Co-Authors: Lu Zhang, Frans Coenen, Paul H Leng
    Abstract:

    The k-Nearest Neighbour (k-NN) method is a typical lazy learning paradigm for solving Classification problems. Although this method was originally proposed as a non-parameterised method, attribute weight setting has been commonly adopted to deal with irrelevant attributes. In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, which is particularly suitable for Binary Classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. To evaluate our method, we carried out a series of experiments on six established data sets. Experiments show that our method is quite practical for various problems and can achieve a stable increase in accuracy over the standard k-NN method as well as a competitive performance. Another merit of the method is that it can use small training sets.

  • an attribute weight setting method for k nn based Binary Classification using quadratic programming
    European Conference on Artificial Intelligence, 2002
    Co-Authors: Lu Zhang, Frans Coenen, Paul H Leng
    Abstract:

    In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, which is particular suitable for Binary Classification problems. Our method formalises the attribute weight setting problem as a quadratic programming problem and exploits commercial software to calculate attribute weights. Experiments show that our method is quite practical for various problems and can achieve a competitive performance. Another merit of the method is that it can use small training sets.