Reliable Evaluation of Neural Network for Multiclass Classification of Real-world Data
This paper presents a systematic evaluation of Neural Network (NN) for classification of real-world data. In the field of machine learning, it is often seen that a single parameter that is 'predictive accuracy' is being used for evaluating the performance of a classifier model. However, this parameter might not be considered reliable given a dataset with very high level of skewness. To demonstrate such behavior, seven different types of datasets have been used to evaluate a Multilayer Perceptron (MLP) using twelve(12) different parameters which include micro- and macro-level estimation. In the present study, the most common problem of prediction called 'multiclass' classification has been considered. The results that are obtained for different parameters for each of the dataset could demonstrate interesting findings to support the usability of these set of performance evaluation parameters.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationSimilar Papers 제목 키워드 기반
Classification Performance Metric Elicitation and its Applications
Given a learning problem with real-world tradeoffs, which cost function should the model be trained to optimize? This is the metric selection problem in machine learning. Despite its practical interest, there is limited …
Binary ClassificationClassificationLightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm
Multiclass decomposition splits a multiclass classification problem into a series of independent binary learners and recomposes them by combining their outputs to reconstruct the multiclass classification results. Three …
ClassificationGeneral ClassificationBlackbox Post-Processing for Multiclass Fairness
Applying standard machine learning approaches for classification can produce unequal results across different demographic groups. When then used in real-world settings, these inequities can have negative societal impacts…
BIG-bench Machine LearningBinary ClassificationClassificationFairnessSimplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classification
We propose a conjugate and calibrated Gaussian process (GP) model for multi-class classification by exploiting the geometry of the probability simplex. Our approach uses Aitchison geometry to map simplex-valued class pro…
Multi-class ClassificationOne-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification
Real-world classification tasks are frequently required to work in an open-set setting. This is especially challenging for few-shot learning problems due to the small sample size for each known category, which prevents e…
ClassificationFew-Shot LearningMeta-LearningOne-Class Classification+1