Improving classification models with context knowledge and variable activation functions

This work proposes two methods to boost the performances of a given classifier: the first one, which works on a Neural Network classifier, is a new type of trainable activation function, that is a function which is adjusted during the learning phase, allowing the network to exploit the data better respect to use a classic activation function with fixed-shape; the second one provides two frameworks to use an external knowledge base to improve the classification results

A survey on modern trainable activation functions

In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest of the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as "trainable", "learnable" or "adaptable" activation functions. They appear to lead to better network performance. Diverse and heterogeneous models of trainable activation function have been proposed in the literature. In this paper, we present a survey of these models. Starting from a discussion on the use of the term "activation function" in literature, we propose a taxonomy of trainable activation functions, highlight common and distinctive proprieties of recent and past models, and discuss main advantages and limitations of this type of approach. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions and some simple local rule that constraints the corresponding weight layers.Comment: Published in "Neural Networks" journal (Elsevier

The Link between Food Traceability and Food Labels in the Perception of Young Consumers in Italy

The research analyzed the perception of food traceability among consumers in Italy and the role of food labels in supporting consumer information about food traceability. The components (health, quality, product origin and many others) that are involved in the concept of food traceability were examined and the most important ones were identified. An online survey (n=511 consumers) was carried out in Milan in the north of Italy. Students and employees from the Bocconi University were selected in order to investigate the relevance of food traceability in consumer purchasing decisions. An ordered logit regression was applied.The findings confirm that consumers are interested in various components of food traceability and look for labels that provide information on the product supply chain. The research confirms that traceability is important in the food market and some types of labels on product features (as product sustainability or origin) are associated with it

Toward the application of XAI methods in EEG-based systems

An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself.Comment: Accepted to be presented at XAI.it 2022 - Italian Workshop on Explainable Artificial Intelligenc

On The Effects Of Data Normalisation For Domain Adaptation On EEG Data

In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions, leading ML systems toward poor generalisation performances. This problem is intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are often used. In fact, EEG signals are highly non-stationary both over time and between different subjects. To overcome this problem, several proposed solutions are based on recent transfer learning approaches such as Domain Adaption (DA). In several cases, however, the actual causes of the improvements remain ambiguous. This paper focuses on the impact of data normalisation, or standardisation strategies applied together with DA methods. In particular, using \textit{SEED}, \textit{DEAP}, and \textit{BCI Competition IV 2a} EEG datasets, we experimentally evaluated the impact of different normalization strategies applied with and without several well-known DA methods, comparing the obtained performances. It results that the choice of the normalisation strategy plays a key role on the classifier performances in DA scenarios, and interestingly, in several cases, the use of only an appropriate normalisation schema outperforms the DA technique.Comment: Published in its final version on Engineering Applications of Artificial Intelligence (EAAI) https://doi.org/10.1016/j.engappai.2023.10620

Hidden Classification Layers: Enhancing linear separability between classes in neural networks layers

In the context of classification problems, Deep Learning (DL) approaches represent state of art. Many DL approaches are based on variations of standard multi-layer feed-forward neural networks. These are also referred to as deep networks. The basic idea is that each hidden neural layer accomplishes a data transformation which is expected to make the data representation "somewhat more linearly separable" than the previous one to obtain a final data representation which is as linearly separable as possible. However, determining the appropriate neural network parameters that can perform these transformations is a critical problem. In this paper, we investigate the impact on deep network classifier performances of a training approach favouring solutions where data representations at the hidden layers have a higher degree of linear separability between the classes with respect to standard methods. To this aim, we propose a neural network architecture which induces an error function involving the outputs of all the network layers. Although similar approaches have already been partially discussed in the past literature, here we propose a new architecture with a novel error function and an extensive experimental analysis. This experimental analysis was made in the context of image classification tasks considering four widely used datasets. The results show that our approach improves the accuracy on the test set in all the considered cases.Comment: Paper accepted on Pattern Recognition Letters journal in Open Access with doi https://www.sciencedirect.com/science/article/pii/S0167865523003264 . Please refer to the published versio

The sustainability in alcohol consumption: the “drink responsibly” frontier

Purpose: The excessive consumption of alcohol in numerous countries in the world, combined with the progressively younger age of the consumers, made it necessary for companies to use instruments of communication aimed at the development of consumption responsibility, so as to prevent reckless behaviour and the health risks thereto associated. The purpose of this paper is to assess the visibility and effectiveness of responsible consumption messages used for the sale of the product “beer” (on packaging and in advertisements); the study used a sample audience made up of teenagers and young adults from southern Italy. Design/methodology/approach: The methodology used was that of the focus group. Three interview sessions were conducted, one dedicated to teenagers, age 16–17 years, and two dedicated to young adult panels, age 20–24 years. A ten-question questionnaire was designed prior to the conduction of the focus groups, and it was used in all the sessions. Findings: The study shows the weak efficacy of the “drink responsibly” communication campaigns carried out by beer manufacturers. The totality of the interviewees failed to remember the existence of the “drink responsibly” messages and, even after supplementary visual stimulation, they were mostly disinterested, defining the fact that companies from the alcoholic drinks industry carry out consumption awareness campaigns as an out-and-out nonsensical contradiction. Originality/value: The survey draws attention to the perception by young audiences of the more recent “drink responsibly” communication campaigns carried out by beer manufacturers, aiming at encouraging a more responsible attitude to alcohol consumption. There still are not many such inquests aimed at determining the response of young people to the use of slogans and commercials connected to responsible drinking in the literature; therefore, this study aimed at filling this gap. In fact, the authors believe this study is important for assessing the effectiveness of such instruments for achieving greater responsibility in the use of alcoholic drinks, so as to develop better awareness in the ranks of youths. Among the new communication strategies that were proposed to the participants, there were video commercials containing responsible consumption messages and the new prohibition marks placed directly on the product labels

Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification

The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier’s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision

Strategies to exploit XAI to improve classification systems

Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. A significant goal of XAI is to improve the performance of AI models by providing explanations for their decision-making processes. However, most XAI literature focuses on how to explain an AI system, while less attention has been given to how XAI methods can be exploited to improve an AI system. In this work, a set of well-known XAI methods typically used with Machine Learning (ML) classification tasks are investigated to verify if they can be exploited, not just to provide explanations but also to improve the performance of the model itself. To this aim, two strategies to use the explanation to improve a classification system are reported and empirically evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Results suggest that explanations built by Integrated Gradients highlight input features that can be effectively used to improve classification performance.Comment: This work has been accepted to be presented to The 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), July 26-28, 2023 - Lisboa, Portuga