Fully supervised training of Gaussian radial basis function networks in WEKA

Radial basis function networks are a type of feedforward network with a long history in machine learning. In spite of this, there is relatively little literature on how to train them so that accurate predictions are obtained. A common strategy is to train the hidden layer of the network using k-means clustering and the output layer using supervised learning. However, Wettschereck and Dietterich found that supervised training of hidden layer parameters can improve predictive performance. They investigated learning center locations, local variances of the basis functions, and attribute weights, in a supervised manner. This document discusses supervised training of Gaussian radial basis function networks in the WEKA machine learning software. More specifically, we discuss the RBFClassifier and RBFRegressor classes available as part of the RBFNetwork package for WEKA 3.7 and consider (a) learning of center locations and one global variance parameter, (b) learning of center locations and one local variance parameter per basis function, and (c) learning center locations with per-attribute local variance parameters. We also consider learning attribute weights jointly with other parameters

Active learning of soft rules for system modelling

Using rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to learning algorithm influence the selection of training examples. The presented method estimates the utility of new experiments based on the knowledge represented by the existing rulebase. An extended rule format allows to deal with uncertainty. Experiments with different artificial system functions show that the presented method improves the model quality respectively decreases the number of experiments needed to reach a specific level of performance

A review of multi-instance learning assumptions

Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area

Market basket analysis of library circulation data

“Market Basket Analysis” algorithms have recently seen widespread use in analyzing consumer purchasing patterns-specifically, in detecting products that are frequently purchased together. We apply the Apriori market basket analysis tool to the task of detecting subject classification categories that co-occur in transaction records of book borrowed form a university library. This information can be useful in directing users to additional portions of the collection that may contain documents relevant to their information need, and in determining a library’s physical layout. These results can also provide insight into the degree of “scatter” that the classification scheme induces in a particular collection of documents

Alternating model trees

Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes. In this paper, we propose a method for growing alternating model trees, a form of option tree for regression problems. The motivation is that alternating decision trees achieve high accuracy in classification problems because they represent an ensemble classifier as a single tree structure. As in alternating decision trees for classifi-cation, our alternating model trees for regression contain splitter and prediction nodes, but we use simple linear regression functions as opposed to constant predictors at the prediction nodes. Moreover, additive regression using forward stagewise modeling is applied to grow the tree rather than a boosting algorithm. The size of the tree is determined using cross-validation. Our empirical results show that alternating model trees achieve significantly lower squared error than standard model trees on several regression datasets

Online estimation of discrete densities using classifier chains

We propose an approach to estimate a discrete joint density online, that is, the algorithm is only provided the current example, its current estimate, and a limited amount of memory. To design an online estimator for discrete densities, we use classifier chains to model dependencies among features. Each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains. Our experiments on synthetic data show that the approach is feasible and the estimated densities approach the true, known distribution with increasing amounts of data

A study of hierarchical and flat classification of proteins

Automatic classification of proteins using machine learning is an important problem that has received significant attention in the literature. One feature of this problem is that expert-defined hierarchies of protein classes exist and can potentially be exploited to improve classification performance. In this article we investigate empirically whether this is the case for two such hierarchies. We compare multi-class classification techniques that exploit the information in those class hierarchies and those that do not, using logistic regression, decision trees, bagged decision trees, and support vector machines as the underlying base learners. In particular, we compare hierarchical and flat variants of ensembles of nested dichotomies. The latter have been shown to deliver strong classification performance in multi-class settings. We present experimental results for synthetic, fold recognition, enzyme classification, and remote homology detection data. Our results show that exploiting the class hierarchy improves performance on the synthetic data, but not in the case of the protein classification problems. Based on this we recommend that strong flat multi-class methods be used as a baseline to establish the benefit of exploiting class hierarchies in this area