Decisions Of Individual Classifiers Research Articles

LeukoX: Leukocyte Classification Using Least Entropy Combiner (LEC) for Ensemble Learning

This brief proposes a scheme to detect and classify across four different classes of white blood cells (WBC) based on their morphological features by ensemble learning applied over multiple deep neural networks (DNN). Contrary to the DNN based WBC classification schemes, this method makes use of a novel least entropy combiner (LEC) network that effectively combines the individual classifier decisions in order to minimize the cross entropy cost. In this brief, two pretrained DNNs, namely DenseNet121 and ResNet50 are used along with a custom-designed convolutional neural network (CNN) to individually produce the class confidence scores which are fed to the proposed LEC network for the ensemble learning purpose. A dataset comprising of around 12, 500 images of four different WBC types, namely Eosinophil, Lymphocyte, Monocyte, and Neutrophil are considered for training and testing the proposed network. The proposed model yields superior performance than the individual networks. Our results demonstrate that the proposed model achieves an overall test accuracy of 96.67%, with precision and sensitivity scores of 96.73%, and 96.62% respectively and the Matthews correlation coefficient (MCC) and kappa scores being 0.9334 and 0.9550, respectively. The overall model is termed as LeukoX (X: Extended).

Comparison of Bagging Ensemble Combination Rules for Imbalanced Text Sentiment Analysis

The wealth of opinions expressed by users on micro-blogging sites can be beneficial for product manufacturers of service providers, as they can gain insights about certain aspects of their products or services. The most common approach for analyzing text opinion is using machine learning. However. opinion data are often imbalanced, e.g. the number of positive sentiments heavily outnumbered the negative sentiments. Ensemble technique, which combines multiple classification algorithms to make decisions, can be used to tackle imbalanced data to learn from multiple balanced datasets. The decision of ensemble is obtained by combining the decisions of individual classifiers using a certain rule. Therefore, rule selection is an important factor in ensemble design. This research aims to investigate the best decision combination rule for imbalanced text data. Multinomial Naïve Bayes, Complement Naïve Bayes, Support Vector Machine, and Softmax Regression are used for base classifiers, and max, min, product, sum, vote, and meta-classifier rules are considered for decision combination. The experiment is done on several Twitter datasets. From the experimental results, it is found that the Softmax Regression ensemble with meta-classifier combination rule performs the best in all except in one dataset. However, it is also found that the training of the Softmax Regression ensemble requires intensive computational resources.

M-ary Rank Classifier Combination: A Binary Linear Programming Problem.

The goal of classifier combination can be briefly stated as combining the decisions of individual classifiers to obtain a better classifier. In this paper, we propose a method based on the combination of weak rank classifiers because rankings contain more information than unique choices for a many-class problem. The problem of combining the decisions of more than one classifier with raw outputs in the form of candidate class rankings is considered and formulated as a general discrete optimization problem with an objective function based on the distance between the data and the consensus decision. This formulation uses certain performance statistics about the joint behavior of the ensemble of classifiers. Assuming that each classifier produces a ranking list of classes, an initial approach leads to a binary linear programming problem with a simple and global optimum solution. The consensus function can be considered as a mapping from a set of individual rankings to a combined ranking, leading to the most relevant decision. We also propose an information measure that quantifies the degree of consensus between the classifiers to assess the strength of the combination rule that is used. It is easy to implement and does not require any training. The main conclusion is that the classification rate is strongly improved by combining rank classifiers globally. The proposed algorithm is tested on real cytology image data to detect cervical cancer.

Interpretation of microbiota-based diagnostics by explaining individual classifier decisions

BackgroundThe human microbiota is associated with various disease states and holds a great promise for non-invasive diagnostics. However, microbiota data is challenging for traditional diagnostic approaches: It is high-dimensional, sparse and comprises of high inter-personal variation. State of the art machine learning tools are therefore needed to achieve this goal. While these tools have the ability to learn from complex data and interpret patterns therein that cannot be identified by humans, they often operate as black boxes, offering no insight into their decision-making process. In most cases, it is difficult to represent the learning of a classifier in a comprehensible way, which makes them prone to be mistrusted, or even misused, in a clinical environment. In this study, we aim to elucidate microbiota-based classifier decisions in a biologically meaningful context to allow their interpretation.ResultsWe applied a method for explanation of classifier decisions on two microbiota datasets of increasing complexity: gut versus skin microbiota samples, and inflammatory bowel disease versus healthy gut microbiota samples. The algorithm simulates bacterial species as being unknown to a pre-trained classifier, and measures its effect on the outcome. Consequently, each patient is assigned a unique quantitative estimation of which species in their microbiota defined the classification of their sample. The algorithm was able to explain the classifier decisions well, demonstrated by our validation method, and the explanations were biologically consistent with recent microbiota findings.ConclusionsApplication of a method for explaining individual classifier decisions for complex microbiota analysis proved feasible and opens perspectives on personalized therapy. Providing an explanation to support a microbiota-based diagnosis could guide decisions of clinical microbiologists, and has the potential to increase their confidence in the outcome of such decision support systems. This may facilitate the development of new diagnostic applications.

Fusion of multiple approximate nearest neighbor classifiers for fast and efficient classification

Abstract The nearest neighbor classifier (NNC) is a popular non-parametric classifier. It is a simple classifier with no design phase and shows good performance. Important factors affecting the efficiency and performance of NNC are (i) memory required to store the training set, (ii) classification time required to search the nearest neighbor of a given test pattern, and (iii) due to the curse of dimensionality it becomes severely biased when the dimensionality of the data is high with finite samples. In this paper we propose (i) a novel pattern synthesis technique to increase the density of patterns in the input feature space which can reduce the curse of dimensionality effect, (ii) a compact representation of the training set to reduce the memory requirement, (iii) a weak approximate nearest neighbor classifier which has constant classification time, and (iv) an ensemble of the approximate nearest neighbor classifiers where the individual classifier's decisions are combined based on the majority vote. The ensemble has constant classification time upperbound and according to empirical results, it shows good classification accuracy. A comparison based on empirical results is shown between our approaches and other related classifiers.

Human Face Image Recognition: An Evidence Aggregation Approach

In this paper a novel analytically-based face recognition system is presented which allows incorporation of the importance of individual facial components in the recognition task. An image gallery of 40 people was used and the images searched to locate the face area and the head boundary. In this system the eyes are detected using learning graph templates, the mouth is detected using deformable templates, and the location of the nose is found by using integral projections based on the mouth and eye locations. Using a 3D model of a head, the facial rotations are estimated in order for the system to compensate for the rotation. The effect of the facial convexity is examined by using an overall recognition index, and an optimum value is used for the rest of the experiments. Each facial feature provides evidence for a classifier, with varying degrees of reliability. Furthermore, a fuzzy information fusion technique is applied to combine the decisions of individual classifiers with all possible combinations of classifiers. The reliability of each classifier is evaluated by an expert using fuzzy density measures in a training phase. An overall classification is derived using a fuzzy evidence aggregation method. The performance of the system is evaluated for various degrees of facial rotation using a cumulative score. The cumulative score provides the normal recognition rate as well as the rank of the next best matches.